2026-05-02 23:00:05
The cryptocurrency code itself offers immutable (or at least supposedly immutable) solutions for consensus. This means that everyone will eventually agree to create a definitive chain… or will they? Well, not always. Crypto is made of code, but it’s also made of people: developers, node operators, average users, companies. This vibrant crypto community may disagree, and what happens next?
We already have the answer, because it’s happened several times in the past, and it keeps happening now. Most crypto networks are open source, which means anyone can participate in their development and copy their code to create their own versions. If they don’t like the direction that a network is taking, they can build their own thing. Sometimes, it’s even inevitable, and everyone is affected by that disagreement.
Let’s learn more about this.
If you’re an average user, then you likely won’t see this part of your favorite network unless it becomes something impossible to ignore. While you’re sending your daily transactions, behind the curtains, in governance forums, code repositories, or even chats like Discord, developers, node operators, and other “advanced” users are discussing maintenance, changes, new versions, and improvements for that very system.
Some networks, like Bitcoin, don’t have a mechanism for their users to vote on anything, while some others, like Obyte, do offer on-chain governance, a system in which token holders can vote on key parameters. In any case, even if the platform doesn’t have on-chain voting, users can still comment on changes and improvements through those forums and repos (if the network is open-source, of course).
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That’s how debate starts. Developers make their proposals, and they enter a long discussion with the community. Some point out technical risks, others focus on economic incentives, and a few argue from pure philosophy. Programmers shape the code, but node operators (miners, “validators,” or similar) decide what software to run. On the other side, token holders can vote on certain proposals and parameters.
If we’re lucky, an agreement is reached: rejected or accepted proposals, changes to the code, updates. Not everyone will be happy, but the network will stay operational. If we’re not, then…
This is the open-source world, so we have forks. Not the cutlery, but the act of cloning and modifying the code of a certain software into two different versions (or more). Crypto is open-source software, so anyone can do this for any reason. Of course, there’s still an “original” version, handled by certain teams. In the end, nodes and average users decide what implementation (version) is best for them.
Soft forks are the subtler events in disagreements. They are enacted by agreement among miners, without asking all users to agree or upgrade. They introduce new rules to the code, but they also remain compatible with older versions of the software. So, people who don’t upgrade can keep participating in the same chain, avoiding the new functionality. Everyone’s included, the cryptocurrency platform remains the same. Not every change can be implemented as a soft fork, though.
Hard forks are more disruptive. They bring rules impossible to understand by the older software. People who decide not to upgrade are deciding to leave the chain entirely. However, if enough users are interested in keeping it alive, the old chain may survive anyway, as a separate platform. After all, this split may be more social than technical. From that moment on, each chain will evolve independently. \n
It really depends on the size of the disagreement and the community involved. Soft forks are barely felt by anyone, while hard forks are impossible to ignore. You either upgrade or don’t upgrade, and the old chain either silently dies or survives with enough supporters. In the latter case, there’s a silver lining: if a user held some coins before the split, then they’ll be multiplied on both chains (you’ll have the same exact balance in both).
It sounds like free money, but they likely won’t have the same price. Wallets, exchanges, and apps also need to decide which version to support, or if they’ll support both versions. We’ve seen it in the past. After the DAO hack on Ethereum in 2016, a portion of the community voted to “erase” the robbery from chain history. This was seen by many people as a blatant violation of decentralization and immutability.
Therefore, not everyone followed the new Ethereum (ETH) chain. Instead, they stayed behind and founded what we know today as Ethereum Classic (ETC).
Both chains share the same history until the split in 2016, and the holders of ETH at that point also received balances in ETC. Since then, ETH and ETC have followed very different paths, with very different teams. For instance, ETC is still mineable with a Proof-of-Work (PoW) algorithm, while ETH migrated to Proof-of-Stake (PoS) with “validators”.
In any case, everyone is free to use and participate in both chains if they want to. It’s not obligatory to “pick a side.” The main consequence of this kind of disagreement is that we’ll have more options available.
We can say that not every fork is so dramatic. Oftentimes, the disagreement occurs long before a new chain exists, and it’s actually the reason why the new chain is created in the first place. Bitcoin, for example, lacked a lot of functionality that other coins have offered over the years. The whitepapers of those coins often started as improvement proposals for Bitcoin that were rejected, or as attempts to add bold changes and new features.
Obyte, by the way, is one of those coins. Inspired by Bitcoin in 2016, it’s not a clone of it. It added functions like user-readable smart contracts, conditional payments, a privacy coin, customized tokens, and decentralized finance. It also replaced the blockchain with a Directed Acyclic Graph (DAG) structure. This system removes the need for middlemen such as miners or “validators,” providing a higher degree of decentralization and censorship resistance.
Disagreement, in crypto, becomes a source of resilience. No single group has full control, and no single decision can reshape the entire system without consent. Users, developers, and businesses all play a role in shaping outcomes through their choices.
Crypto networks don’t need perfect agreement to function. They move forward through negotiation, conflict, and occasional splits. All this leads to real innovation.
:::info Featured Vector Image by Freepik
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2026-05-02 22:01:11
Data scraping is the process of extracting data from websites. It matters for gathering large datasets for analysis, market research, or content aggregation, providing valuable insights from publicly available web information for various applications.
Ever since Google Web Search API deprecation in 2011, I've been searching for an alternative. I need a way to get links from Google search into my Python script. So I made my own, and here is a quick guide on scraping Google searches with requests and Beautiful Soup.
Learn how to scrape the web using scripts written in node.js to automate scraping data off of the website and using it for whatever purpose.
Scraping ChatGPT with Python
A Quick Method To Extract Tweets and Replies For Free
Glassdoor is one of the biggest job markets in the world but can be hard to scrape. In this article, we'll legally extract job data with Python & Beautiful Soup
Early January 2022, I spontaneously bought a pager. I looked into the US pager market, and to my surprise…
With the massive increase in the volume of data on the Internet, this technique is becoming increasingly beneficial in retrieving information from websites and applying them for various use cases. Typically, web data extraction involves making a request to the given web page, accessing its HTML code, and parsing that code to harvest some information. Since JavaScript is excellent at manipulating the DOM (Document Object Model) inside a web browser, creating data extraction scripts in Node.js can be extremely versatile. Hence, this tutorial focuses on javascript web scraping.
Learn how to execute web scraping on Twitter using the snsscrape Python library and store scraped data automatically in database by using HarperDB.
With a Scriptable app, it’s possible to create a native iOS widget even with basic JavaScript knowledge.
To scrape a website, it’s common to send GET requests, but it's useful to know how to send data. In this article, we'll see how to start with POST requests.
There are numerous ways that AI can help us in data scraping and data analysis. Check out these tools and methods!
Web scraping has broken the barriers of programming and can now be done in a much simpler and easier manner without using a single line of code.
A while ago I was trying to perform an analysis of a Medium publication for a personal project. But getting the data was a problem – scraping only the publication’s home page does not guarantee that you get all the data you want.
These extensions for scraping Google maps can be used for a number of purposes in various situations that can be either data collection or market research.
La necesidad de extraer datos de sitios web está aumentando. Cuando realizamos proyectos relacionados con datos, como el monitoreo de precios, análisis de negocios o agregador de noticias, siempre tendremos que registrar los datos de los sitios web. Sin embargo, copiar y pegar datos línea por línea ha quedado desactualizado. En este artículo, le enseñaremos cómo convertirse en un "experto" en la extracción de datos de sitios web, que consiste en hacer web scraping con python.
Check out this step-by-step guide on how to build your own LinkedIn scraper for free!
When you talk about web scraping, PHP is the last thing most people think about.
La necesidad de extraer datos de sitios web está aumentando. Cuando realizamos proyectos relacionados con datos, como el monitoreo de precios, análisis de negocios o agregador de noticias, siempre tendremos que registrar los datos de los sitios web. Sin embargo, copiar y pegar datos línea por línea ha quedado desactualizado. En este artículo, le enseñaremos cómo convertirse en un "experto" en la extracción de datos de sitios web, que consiste en hacer web scraping con python.
An easy tutorial showcasing the power of puppeteer and browserless. Scrape Amazon.com to gather prices of specific items automatically!
The most talented developers in the world can be found on GitHub. What if there was an easy, fast and free way to find, rank and recruit them? I'll show you exactly how to to this in less than a minute using free tools and a process that I've hacked together to vet top tech talent at BizPayO.
How to scrape Kasada-protected websites with Python and other tools, both free and commercial
Market-aware agents must discover and verify live external data. Learn why Instant Knowledge Acquisition is required for accuracy and scale
Learn why you should set a user agent when scraping the web and discover the best user agent for web scraping
Too lazy to scrape nlp data yourself? In this post, I’ll show you a quick way to scrape NLP datasets using Youtube and Python.
A brief comparison between Selenium and Playwright from a web scraping perspective. Which one is the most convenient to use?
Scraping the web is about extracting data in a clean and readable format that developers deploy to read and download an entire web page of its data ethically
Con el advenimiento de los grandes datos, las personas comienzan a obtener datos de Internet para el análisis de datos con la ayuda de rastreadores web. Hay varias formas de hacer su propio rastreador: extensiones en los navegadores, codificación de python con Beautiful Soup o Scrapy, y también herramientas de extracción de datos como Octoparse.
We need to talk about the grim reality of content scraping—a cybercrime undermining creators.
Learn the fundamental distinctions between web crawling and web scraping, and determine which one is right for you.
Scraping football data (soccer in the US) is a great way to build comprehensive datasets to help create stats dashboards. Check out our football data scraper!
Can modern AI systems fully automate web data collection and analysis? Let’s delve deeper into ML and web scraping to see if this is more than just a new hype.
How often have you wanted a piece of information and have turned to Google for a quick answer? Every piece of information that we need in our daily lives can be obtained from the internet. You can extract data from the web and use it to make the most effective business decisions. This makes web scraping and crawling a powerful tool. If you want to programmatically capture specific information from a website for further processing, you need to either build or use a web scraper or a web crawler. We aim to help you build a web crawler for your own customized use.
How to build a data scrapping application using Puppeteer, Node.js, PostgreSQL, and Aptible.
While building ScrapingBee I'm always checking different forums everyday to help people about web scraping related questions and engage with the community.
In the last few years, web scraping has been one of my day to day and frequently needed tasks. I was wondering if I can make it smart and automatic to save lots of time. So I made AutoScraper!
As the CEO of a proxy service and data scraping solutions provider, I understand completely why global data breaches that appear on news headlines at times have given web scraping a terrible reputation and why so many people feel cynical about Big Data these days.
Online Shopping for various commodities is no more a luxury but has rather become a necessity now. Getting your desired product on your doorstep has made it easier for consumers to shop effortlessly. As a result, several niche e-commerce or generic shopping sites pop up every year. This trend is not limited to some specific region rather it’s a global phenomenon now, as more and more people are preferring online shopping over visiting outlets due to traffic congestions and ease of purchasing. This is why it’s predicted that by 2021, overall 15.5% of sales will be generated via online websites.
How to gather data without those pesky databases.
What are alternative data and how to use web scraping to build datasets for financial markets?
Everything you need to know to automate, optimize and streamline the data collection process in your organization!
No-Code tools for collecting data for your Data Science project
Usually forgotten in all Data Science masters and courses, Web Scraping is, in my honest opinion a basic tool in the Data Scientist toolset, as is the tool for getting and therefore using external data from your organization when public databases are not available.
Scrapy is an application framework for crawling web sites and extracting structured/unstructured data which can be used for a wide range of applications such as data mining, information processing or historical archival.As we all know, this is the age of “Data”. Data is everywhere, and every organisation wants to work with Data and take its business to a higher level. In this scenario Scrapy plays a vital role to provide Data to these organisations so that they can use it in wide range of applications. Scrapy is not only able to scrap data from websites, but it is able to scrap data from web services.
A quick introduction to web scraping, what it is, how it works, some pros and cons, and a few tools you can use to approach it
Welcome to the new way of scraping the web. In the following guide, we will scrape BestBuy product pages, without writing any parsers, using one simple library: Scrapezone SDK.
Hi Devs!
Suppose you want to get large amounts of information from a website as quickly as possible. How can this be done?
Learn how to emulate a normal user request and scrape Google Search Console data using Python and Beautiful Soup.
While there are a few different libraries for scraping the web with Node.js, in this tutorial, i'll be using the puppeteer library.
Turning Instagram into data: A fun journey to collect and graph likes and comments using network requests and Python for an ego-boosting data analysis.
Learn how to scrape real estate listings from Domain.com.au using an Apify actor. Extract property details, pricing, agent info, and more.
As Data Scientists, people tend to think what they do is developing and experimenting with sophisticated and complicated algorithms, and produce state of the art results. This is largely true. It is what a data scientist is mostly proud of and the most innovative and rewarding part. But what people usually don’t see is the sweat they go through to gather, process, and massage the data that leads to the great results. That’s why you can see SQL appears on most of the data scientist position requirements.
La necesidad de crawling datos web ha aumentado en los últimos años. Los datos crawled se pueden usar para evaluación o predicción en diferentes campos. Aquí, me gustaría hablar sobre 3 métodos que podemos adoptar para scrape datos desde un sitio web.
Scraping Amazon is challenging. Hence, having the right tools is crucial. I compared three tools based on their price, performance, and features.
Are you looking for a method of scraping Amazon reviews and do not know where to begin with? In that case, you may find this blog very useful in scraping Amazon reviews. In this blog, we will discuss scraping amazon reviews using Scrapy in python. Web scraping is a simple means of collecting data from different websites, and Scrapy is a web crawling framework in python.
Learn everything you need to know about Data Scraping via these 53 free HackerNoon stories.
if you’re an Australian adult on Facebook, your public photos, posts, and other data are being scraped to train their AI models.
Today, We're going to build a script that scrapes Twitter to gather stock ticker symbols. We'll use those symbols to scrape yahoo finance for stock Options data. To ensure we can download all the Options data, we’ll make each web request with High Availability Onion Routing. In the end, we’ll do some Pandas magic to pull the first out of the money call contract for each symbol into the final watchlist.
Previously published at https://www.octoparse.es/blog/15-preguntas-frecuentes-sobre-web-scraping
As a marketer, you probably know that social media marketing is part art, part science.
Cloudflare's AI Labyrinth has Bankrupted Data Scrapers. A major scraping company lost $2.3 million in the first week after the new free tool was launched
Get your hands on excellent manually annotated datasets with Google Sheets or Python
This article discusses the security risks of using auto-increment fields in API responses and methods to prevent data leaks and protect business metrics.
Por favor clic el artículo original：http://www.octoparse.es/blog/70-fuentes-de-datos-gratuitas-en-2020
Learn how to leverage web scraping in marketing. In this article, we unpack use cases and tips for getting started.
The travel industry is a major service sector in most countries these days. It is also a major employment and revenue provider. This demands a lot of constant innovation and maintenance. The travel industry is a dynamic industry where the needs and preferences of a customer change every moment. The market players in this field need to keep up with the trends in the industry, the choices of the customers and even on the details of their own historical performance to perform better as time progresses. Thus, as you would presume, the companies working in the travel sector need a lot of data from multiple sources and a pipeline to assess and use that data for insights and recommendations.
Visit the /Learn Repo to find the most read blog posts about any technology.
2026-05-02 22:00:53
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2026-05-02 19:01:04
AI-assisted development can feel like engineering, until the reward loop starts driving the workflow itself.
Watching AI move into both work and home life, I keep catching myself on a simple thought: writing code by myself is getting harder, and delegating it to AI is getting easier. AI providers also keep encouraging us to spend more tokens. Managers are pushing teams to use AI more actively. On social media, I regularly see joke videos about CEOs telling people to consume tokens for the sake of consuming tokens. And in large companies, there is already an unspoken competition going on: who uses AI tools more, who automates more, who can show faster that "we are in the game too."
The title makes it obvious where I am going. But I do not want to talk about AI hype, or about fear of AI. I want to talk about the more mundane, engineering-related, and unpleasant side of it: vibe coding very easily turns into gambling.
<cut />
I started my pet project, open-daimon, a couple of years ago because I wanted my own Telegram bot. It was private, had a different name, and performed a very limited set of tasks for me. When Copilot appeared, I decided to continue working on it so I could get better at working with AI, since proper AI access was not expected anytime soon in the enterprise project I was working on at the time.
Eventually, it grew into a framework for routing between local models and OpenRouter models depending on the use case. Now it is turning into something like a Java AI agent from 2023. I already use it in another private pet project of mine, but that is not the point here. The important thing is that its second purpose, learning how to work with AI, has been fulfilled. I have gone all the way from the AI-assistant approach to full-on vibe coding.
I found out two things:
Using the latter did not merely speed me up. I also started noticing that the code I write myself is harder for AI to maintain, while code written by AI is much easier for AI to maintain. By now, this is probably not news to many people, but it gradually restructures your entire workflow.
At first, you are still the one planning the architecture, while the AI only writes boring mappings for you. Then it starts finding bugs for you. Then you realize that AI can write an entire feature better than you do and cover it with tests. You are surprised by how quickly it did it and by the fact that everything works right away, even in a complex project.
And then you start trusting it with architectural decisions. It suggests what is best for you. It becomes harder and harder to force yourself to read everything it writes, to review all the code. And then it reviews the code itself.
I will not focus here on practical rules for vibe coding or AI-assisted development. I also want to leave for later the other things I have noticed while vibe-coding with different models. For example, recently Opus 4.7 praised its own code in a review where that code was not even being called. Right now, I simply want to restate an idea that other authors were already expressing a year ago: vibe coding is gambling in its purest form.
I recommend reading Addy Osmani's article, Vibe Coding is not the same as AI-assisted engineering. It is still relevant. Osmani does not directly call vibe coding gambling, but he describes it as the mode of "This isn't engineering, it's hoping": fast, exciting, full of progress signals, but without enough quality control.
Dopamine is tied not just to winning, but to anticipation and reward prediction. You can read about this, for example, in Wolfram Schultz's paper Updating dopamine reward signals. That is why, every single time, you are sure that this time it will finally do the task you asked for 10 prompts ago, even though you only have five hours left to sleep.
I started my pet project without AI, and even back then it already looked monstrous. When I unleashed AI on it, my goal was to check how viable this would be for a real enterprise production project. On my relatively small project, AI does not create features faster than I do. I also spend a lot of time fixing things that used to work. In total, I do not see a speed gain in this particular mode of work.
The article I linked above explains which mode of working with AI is where it really shines. But what matters to me is this: with AI, I continue the project because of the dopamine loop. On my own, I would have abandoned it long ago. AI has also proved useful as a rubber duck that helps me understand what to do next. So there is a non-obvious upside to the dependency.
This apparent simplicity and acceleration are real. That is why we change our processes to benefit from them. But what worries me more is the dependency on companies.
It is no secret anymore that, right now, we are getting AI at very low prices. When it becomes more expensive, many of us will feel withdrawal. Writing code ourselves will already feel difficult, while using AI may become more expensive than hiring a real person. In other words, companies are hooking us with a cheap first dose. We get used to it, and then quitting becomes hard.
This is not only a developer problem. The goal of companies is to get everyone hooked on new capabilities, and that will happen. Sooner or later, those who know how to use AI will displace those who do not. This has already happened with other technologies and professions.
An accountant who knew how to work with Excel and accounting systems became more productive than one who continued calculating everything manually. A lawyer who knew how to quickly search case law in legal databases and Google outpaced one who relied only on paper reference books. A marketer who mastered analytics, CRM systems, and ad platforms displaced one who continued working "by gut feeling."
But with Google, we did not experience similar emotions. Or did we?
In a 2012 paper, Internet Addiction: A Brief Summary of Research and Practice, the authors relied on earlier work going back to 1995. People were already worried then about new technologies and how they could cause addiction. The article leads to the idea that internet addiction resembles other behavioral addictions: loss of control, excessive use, symptoms similar to withdrawal, an increase in "dose" resembling tolerance, and harm to studies, work, or relationships.
And now, in South Korea, basic mobile internet is being introduced as a right to connectivity.
So are we about to see the same cycle of development again? Or will large corporations gain even more influence this time and charge us for tokens rather than kilobytes, with the price being even higher?
It feels as though AI is already everywhere. The data partially supports this, but with an important caveat: mass awareness has already happened, while mass habit has not.
According to Pew Research, in 2025, 34% of American adults had used ChatGPT, and among people under 30, the figure was 58%. Gallup shows a similar picture at work. In Q1 2026, 50% of U.S. employees used AI at least a few times a year, 28% used it a few times a week or more, and 13% used it daily. Among remote-capable employees, the gap is more noticeable: Gallup's Q4 2025 data showed 66% had used AI, 40% used it frequently, and 19% used it daily.
And that is before we even mention that the internet itself is still not available everywhere. In 2025, roughly 6 billion people used the internet, about 74% of the world's population. Around 2.2 billion people were still offline. These are figures from the International Telecommunication Union, the UN agency for communications.
In the end, I still want to believe in a bright future rather than cyberpunk, although the cyberpunk scenario already seems to have practically materialized in some countries even without AI. Whether we want it or not, we will continue using AI and accelerating until something radical happens.
And that does not necessarily have to be something bad. Perhaps, with increased efficiency, people will still learn something. It seems we did learn something from the emergence of the internet. Maybe the world became a little more educated after all.
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2026-05-02 10:33:05
Last week, we went over Marvel Comics’ subscription service, Marvel Unlimited. So, it would only be fair to check out DC’s subscription service: DC Universe Infinite. They’re pretty similar; if you use one, you can navigate and understand the other. There are some key differences, though, enough to warrant a whole separate article. Here’s what you need to know about DC Universe Infinite.
The subscription service has 3 different plans, each with different content. Let’s go over what each plan has and how much they cost.
The first plan is completely free, so it should come as no surprise that this is the plan with the least amount of content. But you do get some neat stuff. You get a few free comic books to read, and these change from time to time. You also get some comics from their DC GO! imprint. This imprint is specifically made to be read on digital devices. Not bad for a free tier.
Next is the standard plan. This plan can be bought monthly for $7.99 or annually for $74.99. According to the DC Universe Infinite website, here’s everything that comes with this plan: over 27k comics, with new comic books being available 6 months after their release. It’s a bit of a wait, but it could be worse; it could be 8 months or even a year. This plan also allows for a 7-day free trial.
The final plan is Ultra. The website states that this plan has over 35k comics, which include books from imprints such as Vertigo and Black Label. Also, instead of waiting 6 months for new releases, you’ll now have to wait just 30 days. This plan is definitely the one with the most value, but it’s also the priciest. Monthly is $12.99, and annually is $119.99. A 7-day free trial is also available for this plan.
\ These are the 3 plans that potential customers can choose from. Now, let’s take a look at what devices are supported.
In the FAQ section of the website, it says that it’s available on iPhones, iPads, Android phones, and Android tablets. You might notice that there’s one big omission: Amazon Kindle tablets. Yep, like Marvel Unlimited, this service is also not available on these devices. It’s unfortunate, but not a dealbreaker.
\ I’ve personally used this service on iPhone and iPad, and it worked seamlessly. I haven’t tried it on Android devices, but I imagine it would be a similar positive experience.
\ Now, for 2 more important questions:
I would say yes, it’s absolutely worth it. If you’re just a casual DC fan and want to read certain comics from time to time, I would go for the standard plan. If you love reading comics and know you can read a ton in a month, the Ultra plan is the one for you. And whether you want to go monthly or annually is up to you. It’s apparently 23% cheaper to go annually.
\ However, I also know that some people love to alternate between services. One month, it would be Marvel Unlimited, then DC Universe Infinite. I think this might be the smartest way to do it, but there is no right or wrong way. So, choose the right option for you, and start reading!
\
2026-05-02 10:00:41
The science of using computer programs to sift through thousands of data points and then using computer programs to present that data in a visual format.
In 2022, Gartner named Microsoft Power BI the Business Intelligence and Analytics Platforms leader. These are the 13 Best Datasets for Power BI Practice.
3 ways to pull JSON data into a Google Spreadsheet
Mobile phones have always been a staple of corporate communication. In the early days, companies would provide mobile devices to their employees.
Today I'm open sourcing "Grid studio", a web-based spreadsheet application with full integration of the Python programming language.
Classification algorithms learn how to assign class labels to examples (observations or data points), although their decisions can appear opaque.
This story looks into random forest regression in R, focusing on understanding the output and variable importance.
There can be one or many solutions to a given problem, depending on the scenario, As there can be many ways to solve that problem. Think about how do you approach a problem. Lets say you need to do something straight forward like a math multiplication. Clearly there is one correct solution, but many algorithms to multiply, depending on the size of the input. Now, take a more complicated problem, like playing a game(imagine your favorite game, chess, poker, call of duty, DOTA, anything..). In most of these games, at a given point in time, you have multiple moves that you can make, and you choose the one that gives you best possible outcome. In this scenario, there is no one correct solution, but there is a best possible solution, depending on what you want to achieve. Also, there are multiple ways to approach the problem, based on what strategy you choose to have for your game play.
A typical interview process for a data science position includes multiple rounds. Often, one of such rounds covers theoretical concepts, where the goal is to determine if the candidate knows the fundamentals of machine learning.
The theory formulates a mathematical model to optimize the asset allocations to gain the maximum return for a given risk-level.
In this listicle, you'll find some of the best data engineering courses, and career paths that can help you jumpstart your data engineering journey!
In order to understand how a certain metric varies over time and to predict future values, we will look at the 10 Best Datasets for Time Series Analysis.
Topic modeling is an unsupervised machine learning technique that can automatically identify different topics present in a document (textual data). Data has become a key asset/tool to run many businesses around the world. With topic modeling, you can collect unstructured datasets, analyzing the documents, and obtain the relevant and desired information that can assist you in making a better decision.
On my self-taught programming journey, my interests lie within machine learning (ML) and artificial intelligence (AI), and the language I’ve chosen to master is Python.
We will focus on MSE and MAE metrics, which are frequently used model evaluation metrics in regression models.
Learn how to save time and eliminate manual data imports in Google Sheets by automatically connecting and importing data from external sources.
Image annotation is one of the most important tasks in computer vision. With numerous applications, computer vision essentially strives to give a machine eyes – the ability to see and interpret the world. At times, machine learning projects seem to unlock futuristic technology we never thought possible. AI-powered applications like augmented reality, automatic speech recognition, and neural machine translation have the potential to change lives and businesses around the world. Likewise, the technologies that computer vision can give us (autonomous vehicles, facial recognition, unmanned drones) are extraordinary.
Ever since Google Web Search API deprecation in 2011, I've been searching for an alternative. I need a way to get links from Google search into my Python script. So I made my own, and here is a quick guide on scraping Google searches with requests and Beautiful Soup.
There are multiple approaches that you might take to create Artificial Intelligence, based on what we hope to achieve with it and how will we measure its success. It ranges from extremely rare and complex systems, like self driving cars and robotics, to something that is a part of our daily lives, like face recognition, machine translation and email classification.
Feature Selection in python is the process where you automatically or manually select the features in the dataset that contribute most to your prediction.
Importance of C++ in Data Science and Big Data
Learn how to easily extract valuable information from Google Maps using Python with our step-by-step guide.
Computer vision enables computers to understand the content of images and videos. The goal in computer vision is to automate tasks that the human visual system can do.
In a real-world setting, you often only have a small dataset to work with. Models trained on a small number of observations tend to overfit and produce inaccurate results. Learn how to avoid overfitting and get accurate predictions even if available data is scarce.
Big data may seem like any other buzzword in business, but it’s important to understand how big data benefits a company and how it’s limited.
Facial recognition-based authentication to verify a user in a web application is discussed in a beginner-friendly manner using FaceIO APIs.
Google Colab and VS Code are popular editor tools. Learn how you can use Google Colab with VS Code and take advantage of a full-fledged code editor.
RAIN executives give a full breakdown of the build out and power of AI Voice Assistants.
As if taking a picture wasn’t a challenging enough technological prowess, we are now doing the opposite: modeling the world from pictures. I’ve covered amazing AI-based models that could take images and turn them into high-quality scenes. A challenging task that consists of taking a few images in the 2-dimensional picture world to create how the object or person would look in the real world.
We’re going to use Term Frequency — Inverse Document Frequency (TF-IDF) to find the most important sentences in a BBC news article. Then we are going to implement this algorithm into a quick & easy Firefox extension.
Hello, Machine Learning community!
There're numerous JavaScript charting libraries. To make your life easier, I decided to share my picks. Check out the best JS libraries for creating web charts!
The Datasets library from hugging Face provides a very efficient way to load and process NLP datasets from raw files or in-memory data. These NLP datasets have been shared by different research and practitioner communities across the world.
A curated list of courses to learn data science, machine learning, and deep learning fundamentals.
Text classification datasets are used to categorize natural language texts according to content. For example, think classifying news articles by topic, or classifying book reviews based on a positive or negative response. Text classification is also helpful for language detection, organizing customer feedback, and fraud detection. Though time consuming when done manually, this process can be automated with machine learning models. The result saves companies time while also providing valuable data insights.
The 2019–20 coronavirus pandemic is an ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak was first identified in Wuhan, Hubei, China, in December 2019, and was recognized as a pandemic by the World Health Organization (WHO) on 11 March 2020.
While the release of GPT-3 marks a significant milestone in the development of AI, the path forward is still obscure. There are still certain limitations to the technology today. Here are six of the major limitations facing data scientists today.
Data is very important in building computer vision models and these are the 10 Biggest Datasets for Computer Vision.
Data is a central piece of the climate change debate. With the climate change datasets on this list, many data scientists have created visualizations and models to measure and track the change in surface temperatures, sea ice levels, and more. Many of these datasets have been made public to allow people to contribute and add valuable insight into the way the climate is changing and its causes.
On Hacker Noon, I will be sharing some of my best-performing machine learning articles. This listicle on datasets built for regression or linear regression tasks has been upvoted many times on Reddit and reshared dozens of times on various social media platforms. I hope Hacker Noon data scientists find it useful as well!
A data science interview consists of multiple rounds. One of such rounds involves theoretical questions, which we covered previously in 160+ Data Science Interview Questions.
Learn to build AI ruled-based chatbot with a simple tutorial that can be showcased in your Portfolio.
How I learned to stop using pandas and love SQL.
This blog post explains the most intricate data warehouse SQL techniques in detail.
Here’s DreamFusion, a new Google Research model that can understand a sentence enough to generate a 3D model of it.
Dummy data is randomly generated data that can be substituted for live data. Whether you are a Developer, Software Engineer, or Data Scientist, sometimes you need dummy data to test what you have built, it can be a web app, mobile app, or machine learning model.
A Quick Method To Extract Tweets and Replies For Free
Best stock market data APIs for data scientists and algorithmic traders: Alpha Vantage, Barchart OnDemand, Tradier, Intrinio, and Xignite.
For those looking to build predictive models, this article will introduce 10 stock market and cryptocurrency datasets for machine learning.
Processing large data, e.g. for cleansing, aggregation or filtering is done blazingly fast with the Polars data frame library in python thanks to its design.
Early January 2022, I spontaneously bought a pager. I looked into the US pager market, and to my surprise…
Get savvy with Pandas DataFrame updates & appends using dictionaries for smoother data tinkering.
AI is being used to help analysts with routine tasks. But it can also be a real contender on the analytics team.
Spark is the name of the engine to realize cluster computing while PySpark is the Python's library to use Spark.
While I'm usually a JavaScript person, there are plenty of things that Python makes easier to do. Doing voice recognition with machine learning is one of those.
Learn how to execute web scraping on Twitter using the snsscrape Python library and store scraped data automatically in database by using HarperDB.
Multicollinearity refers to the high correlation between two or more explanatory variables, i.e. predictors. It can be an issue in machine learning too.
pandas and matplotlib.
Recently I had to write a script, which should’ve changed some JSON data structure in a PSQL database. Here are some tricks I learned along the way.
An Introduction to Anomaly Detection and Its Importance in Machine Learning
Explore the intricacies of calculating median statistics in sliding windows, a vital tool for real-time data analysis in diverse fields.
Let's conduct a penetration testing on a file with a detailed study analysis of system passwords as part of an ethical hacking engagement.
Explore the evolution of DataOps in data engineering, its parallels with DevOps, challenges it addresses, and best practices. Transformative future of DataOps.
There was a time when the data analyst on the team was the person driving digitalization in an adventurous data quest…and then the engineers took over.
Linear Regression is generally classified into two types:
Motivation
To scrape a website, it’s common to send GET requests, but it's useful to know how to send data. In this article, we'll see how to start with POST requests.
Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving.
Performant machine learning models require high-quality data. And training your machine learning model is not a single, finite stage in your process. Even after you deploy it in a production environment, it’s likely you will need a steady stream of new training data to ensure your model’s predictive accuracy over time.
Here are the Top 9 ML, AI, and Data Science Internships to consider for 2022 if you want to get into any of these very lucrative fields in computer science.
Web apps are still useful tools for data scientists to present their data science projects to the users. Since we may not have web development skills, we can use open-source python libraries like Streamlit to easily develop web apps in a short time.
When you absolutely need to have a perfect replica of your data for data exploration, CockroachDB and MinIO is your winning strategy.
Karate Club is an unsupervised machine learning extension library for the NetworkX Python package. See the documentation here.
Today, with open source machine learning software libraries such as TensorFlow, Keras or PyTorch we can create neural network, even with a high structural complexity, with just a few lines of code. Having said that, the Math behind neural networks is still a mystery to some of us and having the Math knowledge behind neural networks and deep learning can help us understand what’s happening inside a neural network. It is also helpful in architecture selection, fine-tuning of Deep Learning models, hyperparameters tuning and optimization.
The README file is the very first item that developers examine when they access your Data Science project hosted on GitHub. Every developer should begin their exploration of your Data Science project by reading the README file. This will tell them everything they need to know, including how to install and use your project, how to contribute (if they have suggestions for improvement), and everything else.
A big question for Machine Learning and Deep Learning apps developers is whether or not to use a computer with a GPU, after all, GPUs are still very expensive. To get an idea, see the price of a typical GPU for processing AI in Brazil costs between US $ 1,000.00 and US $ 7,000.00 (or more).
Whether you’re a beginner looking for introductory articles or an intermediate looking for datasets or papers about new AI models, this list of machine learning resources has something for everyone interested in or working in data science. In this article, we will introduce guides, papers, tools and datasets for both computer vision and natural language processing.
For the first KDnuggets post on Hacker Noon, we bring you a lighter fare of very nerdy computer humor from the series of self-referential jokes started on Twitter earlier this week. Here are some of our favorites.
If you do understand all of the jokes, then you congratulate yourself on having excellent knowledge of Data Science and Machine Learning! If you have actually laughed at 2 or more jokes, then you have earned MS in Computer Humor! If you just smirked, you probably have a Ph.D. And I have a great joke about AGI, but it will be ready in 10 years.
Enjoy, and if you have more, add them in comments below!
Yann LeCun, @ylecun
Although commonplace, logs hold critical information about system operations and are a valuable source of debugging and troubleshooting information.
In this tutorial, I will guide you on how to detect emotions associated with textual data and how can you apply it in real-world applications.
MinIO is a high-performance, cloud native object store. Because of this, MinIO can become the global datastore for Snowflake customers, wherever their data sits
More recently on my data science journey I have been using a low grade consumer GPU (NVIDIA GeForce 1060) to accomplish things that were previously only realistically capable on a cluster - here is why I think this is the direction data science will go in the next 5 years.
Data analytics can transform how businesses operate. With companies having tons of data today , data analytics can help companies deliver valuable products and services to customers.
In Reinforcement Learning (RL), agents are trained on a reward and punishment mechanism. The agent is rewarded for correct moves and punished for the wrong ones. In doing so, the agent tries to minimize wrong moves and maximize the right ones.
True story from retail finance about LTV modeling with ML algorithms for evaluation customer acquisition channels.
MinIO is the right choice for Quickwit because of its industry-leading performance and scalability.
After reading this article, you will be able to create a search engine for similar images for your objective from scratch
Learn how to approach data-driven measurement properly. See what unexpected results we got in a bank and get insights for your own data analytics journey.
Hugging Face offers solutions and tools for developers and researchers. This article looks at the Best Hugging Face Datasets for Building NLP Models.
This post focuses on how Iceberg and MinIO complement each other and how various analytic frameworks (Spark, Flink, Trino, Dremio, Snowflake) can leverage them.
The combination of MinIO and Delta Lake enables enterprises to have a multi-cloud data lake that serves as a consolidated single source of truth.
From no limits on unstructured data to having greater control over serving models, here are some reasons why AI is built on object storage.
Streamlit, Dash, Reflex and Rio. A comparison of python web app frameworks .
We’ve all heard about GPT-3 and have somewhat of a clear idea of its capabilities. You’ve most certainly seen some applications born strictly due to this model, some of which I covered in a previous video about the model. GPT-3 is a model developed by OpenAI that you can access through a paid API but have no access to the model itself.
Web scraping has broken the barriers of programming and can now be done in a much simpler and easier manner without using a single line of code.
To help you build object recognition models, scene recognition models, and more, we’ve compiled a list of the best image classification datasets. These datasets vary in scope and magnitude and can suit a variety of use cases. Furthermore, the datasets have been divided into the following categories: medical imaging, agriculture & scene recognition, and others.
Metabase is a business intelligence tool for your organisation that plugs in various data-sources so you can explore data and build dashboards. I'll aim to provide a series of articles on provisioning and building this out for your organisation. This article is about getting up and running quickly.
You will find out how we at inDrive currently handle regular vehicle verifications. Every month, we ask our users to take a picture of their car.
When you think of Artificial Intelligence, the first thing that comes to mind is either Robots or Machines with Brains or Matrix or Terminator or Ex Machina or any of the other amazing concepts having machines that can think. This is an appropriate but vague understanding of Artificial Intelligence. In this article we’ll see what A.I. really is and how the definition has changed in the past.
Researchers have been studying the possibilities of giving machines the ability to distinguish and identify objects through vision for years now. This particular domain, called Computer Vision or CV, has a wide range of modern-day applications.
Using MinIO for Hudi storage paves the way for multi-cloud data lakes and analytics.
Dashboard with different visualizations allows you to compare data and show changes and tendencies. In this tutorial I wil explain why and how to build one.
As an aspiring data scientist, the best way for you to increase your skill level is by practicing. And what better way is there for practicing your technical skills than making projects.
If you've been on LinkedIn anytime in the past several months, you've probably come across the infamous "certification post."
In my free time, I am attempting to build my own smart home devices. One feature they will need is speech recognition. While I am not certain yet as to how exactly I want to implement that feature, I thought it would be interesting to dive in and explore different options. The first I wanted to try was the SpeechRecognition library.
Learn about ColBERT, a new way of scoring passage relevance using a BERT language model that substantially solves the problems with dense passage retrieval.
Looking for MongoDB data visualization tool? There are plenty of options but firstly its better to explore what kinds of solutions there are on the market.
Every business has its goals and the path to attaining those goals usually lies in data, it’s why our data is so important today.
You have a plain old TensorFlow model that’s too computationally expensive to train on your standard-issue work laptop. I get it. I’ve been there too, and if I’m being honest, seeing my laptop crash twice in a row after trying to train a model on it is painful to watch.
RNN is one of the popular neural networks that is commonly used to solve natural language processing tasks.
The OLAP experience of an automobile manufacturer.
Master key time series feature engineering techniques to enhance predictive models in finance, healthcare & more with our comprehensive guide.
Pynecone is an open-source framework to build web apps in pure Python and deploy with a single command.
Looking for sentiment analysis companies or sentiment annotation tools? If so, you’ve come to the right place. This guide will briefly explain what sentiment analysis is, and introduce companies that provide sentiment annotation tools and services.
Gato from DeepMind was just published! It is a single transformer that can play Atari games, caption images, chat with people, control a real robotic arm, and more! Indeed, it is trained once and uses the same weights to achieve all those tasks. And as per Deepmind, this is not only a transformer but also an agent. This is what happens when you mix Transformers with progress on multi-task reinforcement learning agents.
With the help of SystemD and MinIO you can automate your cloud object storage deployments and ensure the service lifecycle is managed smoothly and successfully.
Building a production-grade RAG application demands a suitable data infrastructure to store, version, process, evaluate, and query chunks of data.
Douglas Crockford and Chip Morngingstar created the data exchange format that is now known as JSON.
HOG - Histogram of Oriented Gradients (histogram of oriented gradients) is an image descriptor format, capable of summarizing the main characteristics of an image, such as faces for example, allowing comparison with similar images.
For those looking to analyze crime rates or trends over a specific area or time period, we have compiled a list of the 16 best crime datasets made available for public use.
It is often very difficult for AI researchers to gather social media data for machine learning. Luckily, one free and accessible source of SNS data is Twitter.
Recently, Amazon released a new tool, called Honeycode, which lets customers quickly build mobile and web applications — with no coding required. This came a few months after Google’s acquisition of the no-code mobile-app-building platform, AppSheet. While these moves surprised many, they’re in line with a larger trend I’ve observed, one that’s growing strong in all sectors, even amidst economic turmoil.
Hypothesis tests are significant for evaluating answers to questions concerning samples of data.
Counts are everywhere, so no matter your background, these data distributions will come in handy.
Machine learning models are usually developed in a training environment (online or offline). And you can then deploy them and use them with live data.
Unlock the power of AI with these 9 free tools! Boost productivity, improve decision-making, & enhance your personal life.
Transformer models have become the defacto standard for NLP tasks. As an example, I’m sure you’ve already seen the awesome GPT3 Transformer demos and articles detailing how much time and money it took to train.
How does the ZIP format work?
In Digital Healthcare data platforms, data quality is no longer a nice-to-have — it is a hard requirement.
Using ML to analyze and predict CLV offers more accurate, actionable insights by learning from behavioral data at scale.
Human behaviour describes how people interact and in this article, we will look at the 8 Best Human Behaviour Datasets for Machine Learning.
Learn how to build an NLP model and deploy it with a fast web framework for building APIs called FastAPI.
Having fun while developing is necessary for programmers and developers. No matter how much serious or tough the situation is, one should always take things lightly when it comes to software development.
A complete setup of a ML project using version control (also for data with DVC), experiment tracking, data checks with deepchecks and GitHub Action
Semi-supervised learning is the type of machine learning that is not commonly talked about by data science and machine learning practitioners but still has a very important role to play.
What is Linear Regression ?
A quick walkthrough of the three frameworks in probability viz. classical, frequentist and Bayesian through an example.
Is graph really the new star schema? What do graphs like to non-insiders, and what attracts them to the community, methodologies, applications, and innovation?
If you want to learn Microsoft Excel, a productivity tool for IT professionals, and looking for free online courses, then you have come to the right place.
Here are the top 20 Coursera Courses and Certifications to Learn Data Science, Cloud Computing, and Python.
Is Astronomy data science?
Access to training data is one of the largest blockers for many machine learning projects. Luckily, for various different projects, we can use data augmentation to increase the size of our training data many times over.
A practical guide to using machine learning in business, from defining problems and choosing models to deployment, monitoring, and delivering real value.
This is actually an assignment from Jeremy Howard’s fast.ai course, lesson 5. I’ve showcased how easy it is to build a Convolutional Neural Networks from scratch using PyTorch. Today, let’s try to delve down even deeper and see if we could write our own nn.Linear module. Why waste your time writing your own PyTorch module while it’s already been written by the devs over at Facebook?
This paper talks to the rise and fall of Hadoop HDFS and why high-performance object storage is a natural successor in the big data world.
A deep dive into 5 early adopters of vector search- Pinterest, Spotify, eBay, Airbnb and Doordash- who have integrated AI into their applications.
Researchers use an algebraic acme called “Losses” in order to optimise the machine learning space defined by a specific use case.
In version 3.2.0, DolphinScheduler introduces a series of new features and improvements, significantly enhancing its stability.
Scientists use geospatial analytics to build visualizations such as maps, graphs and cartograms. These are the Best Public Datasets for Geospatial Analytics.
Research suggests that data scientists spend a whopping 80% of their time preprocessing data and only 20% on actually building machine learning models. With that in mind, it’s no wonder why the machine learning community was quick to embrace crowdsourcing for data labeling. Crowdsourcing helps break down large and complex machine learning problems into smaller and simpler tasks for a large distributed workforce.
In the process of building a Machine Learning model, there is a trade-off between bias and variance.
Andrew Ng likes it, you probably will too!
DynamoDB's secondary indexes are a powerful tool for enabling new access patterns for your data.
Explore time series analysis: from cross-validation, decomposition, transformation to advanced modeling with ARIMA, Neural Networks, and more.
We, humans, are experiencing tailor-made services which have been engineered right for us, we are not troubled personally, but we are doing one thing every day, which is kind of helping this intelligent machine work day and night just to make sure all these services are curated right and delivered to us in the manner we like to consume it.
In this tutorial, we will develop a simple Agent that accesses multiple data sources and invokes data retrieval when needed.
Malloy is a new experimental language for describing data relationships and transformations created by the developer of Looker.
For analytical use cases, you can gain significant performance and cost advantages by syncing the DynamoDB table with a different tool or service like Rockset.
This guide will look into the ten best B2B data providers that can fuel your business strategy and help you expand your customer base.
Product manager interviews usually include a section on metrics. As a data scientist at Uber, I’ve often given or helped friends prepare for these interviews. The difference between candidates who crush the metric questions and those who struggle turns, as far as I can tell, on whether they have a framework that they can apply.
Binary classification is one of the most common machine learning tasks. In practice, the goal of such tasks often extends beyond simply predicting a class.
How to use Approximate leave-one-out cross-validation for hyperparameter optimization and outlier detection for logistic regression and ridge regression
Tree-based models like Random Forest and XGBoost have become very popular in solving tabular(structured) data problems and gained a lot of tractions in Kaggle competitions lately. It has its very deserving reasons. However, in this article, I want to introduce a different approach from fast.ai’s Tabular module leveraging.
Learn how to combine categorical features in your dataset to improve your machine learning model performance.
This article is a quick introduction to Dagster using a small ML project. It is beginner friendly but might also suit more advanced programmers if they dont know Dagster.
Is your Brain a Data Scientist? Yes, according to the Bayesian Brain Hypothesis, your brain is a Bayesian statistician. Let me explain.
Learn how machine learning advances product analytics — from predicting behavior to optimizing personalized, data-driven decisions.
As we sit down for this exclusive interview, Leonid offers a rare glimpse into the intricate process of weaving the digital fabric that shapes our lives.
Hi folks !! In this post, i will discuss about basic tools and software that one can use to solve a data science problem . If you are new to ML or Data Science or Statistics, Feel free to check out my other blog on ML by clicking on the link below.
Exploratory Data Analysis (EDA) is an essential step in the data science project lifecycle. Here are the top 10 python tools for EDA.
Learn 5 ways to accelerate point queries and 4 methods to further improve concurrency: row storage format, short circuit, prepared statement, and row storage ca
How can we know ours ads are making impact that we aim for? What if targeted ads are not working the way we want them to?
Transformer models have become by far the state of the art in NLP technology, with applications ranging from NER, Text Classification, and Question Answering
We are living in a weird time. Day by day we see more & more people coughing and getting sick, our neighbors, coworkers on Zoom calls, politicians, etc… But here’s when it becomes really, really scary — when you become one of “those” and have no clue what to do. Your reptile brain activates, you enter a state of panic, and engage complete freakout mode. That’s what happened to me this Monday, and I’m not sure I’m past this stage.
Learn how to use MySQL’s SET and ENUM data types effectively. This guide explains their internal behavior, common pitfalls, and best practices
Collecting data from the web can be the core of data science. In this article, we'll see how to start with scraping with or without having to write code.
Have you heard of the data Streamhouse yet? Find out more and learn about Apache Paimon
Everyone is talking about ontologies. Why, what is an ontology actually, and how is it related to graphs?
Pull stock prices from online API and perform predictions using Recurrent Neural Network & Long Short Term Memory (LSTM) with TensorFlow.js framework
So you want to become a data scientist? You have heard so much about data science and want to know what all the hype is about? Well, you have come to the perfect place. The field of data science has evolved significantly in the past decade. Today there are multiple ways to jump into the field and become a data scientist. Not all of them need you to have a fancy degree either. So let’s get started!
How to not get stuck when collecting tabular data from the internet.
Exploring Data Science and Machine Learning (DSML) Platforms
How does the GIF format work?
Explore a product developer's journey in tackling AI bias and fairness. Learn how ethical considerations shape AI design, ensuring technology benefits everyone.
This blog explains about polygon data, its benefits and how it is widely used in geomarketing, indoor mapping, and mobility analysis for orgnaizations.
Google recently announced a new model for automatically generating summaries using machine learning, released in Google Docs that you can already use.
An incredible 87% of data science projects never go live.
PyTorch has gained a reputation as a research-focused framework, and these are the Best PyTorch Datasets for Building Deep Learning Models available today.
CRISPR, Quantum, Graphene, Smart Dust, Digital Twins, the Metaverse… You’ve heard about it all. Seen it all. Read it all. Or have you?
In this article, we set sail on a captivating journey through the EDA process, using the legendary Titanic dataset from Kaggle as our North Star.
In this blog, you will learn about the Pickling and Unpickling process, although it is quite simple it is very important and useful.
Here are the five best articles related to artificial intelligence in May posted on Hackernoon.
Uploading 1 million row size large CSV to mongoDB using nodejs stream
An introduction to neural vector search, in comparison to keyword-based search.
Intro
Let’s build a fashion-MNIST CNN, PyTorch style. This is A Line-by-line guide on how to structure a PyTorch ML project from scratch using Google Colab and TensorBoard
Qlik Sense is powerful data visualization and BI software. But sometimes its functions are not enough. Meet the best Qlik Sense extensions to do more with data!
The online data science community is supportive and collaborative. One of the ways you can join the community is to find machine learning and AI Slack groups.
Introduction
Ensemble modelling helps you avoid overfitting by reducing variance in the prediction and minimizing modelling method bias.
The time to start building one's synthetic replacement is now.
Minimalistic Data Structure Sketches
Programming is a complex and multifaceted field that encompasses a wide range of mathematical and computational concepts and techniques.
I am a huge fan of combat sports, with boxing in particular being my favourite. As much as it may appear as a purely physical sport where your sole objective is to either outbox or knock your opponent out, it is far more strategic that one would expect and incorporates an element psychology. Like a chess game, each punch thrown has to be calculated, recklessly overextending yourself might leave you more vulnerable to a counter punch, while being overly passive and defensive might swing the momentum in your opponent’s favour and not get you enough points to win the fight. If you let self-doubt sink in or are intimidated by your opponent you have already lost the battle. On top of all this, you need to remain respectful of the sport and the life threatening dangers it presents. In the words of of Sugar Ray Leonard, 'you don't play boxing'.
Pycaret is an open-source, low code library in python that aims to automate the development of machine learning models.
This headline may seem a bit odd to you. After all, if you’re a data scientist in 2019, you’re already marketable. Since data science has a huge impact on today’s businesses, the demand for DS experts is growing. At the moment I’m writing this, there are 144,527 data science jobs on LinkedIn alone.
Gain entry into IT with knowledge of data science, engineering, cloud computing, cybersecurity, or devops.
Attacking Toxic Comments Kaggle Competition Using Fast.ai
Marketing Mix Modeling is a statistical analysis method used in marketing to determine the optimal allocation of resources.
How tech executives can harness advanced econometric and AI-driven simulation techniques to make informed investment decisions under uncertainty?
When it comes to building an Artificially Intelligent (AI) application, your approach must be data first, not application first.
Imagine — You’re in a system design interview and need to pick a database to store, let’s say, order-related data in an e-commerce system. Your data is structured and needs to be consistent, but your query pattern doesn’t match with a standard relational DB’s. You need your transactions to be isolated, and atomic and all things ACID… But OMG it needs to scale infinitely like Cassandra!! So how would you decide what storage solution to choose? Well, let’s see!
There exist two common log processing solutions within the industry, exemplified by Elasticsearch and Grafana Loki, respectively.
DecentraMind by Web 3.0 or for it? — interview with Mikhail Danieli, project visionary and ambassador about the future of the platform and the company.
Machine learning has become a diverse business tool to enhance the various elements of business operations. Also, it has a significant influence on the performance of the business. Machine learning algorithms are used widely to maintain competition with different industries. However, there is a different type of algorithms for goals and data sets. The selection of an algorithm depends on user role and the purpose. If you are using Linear regression, then you can quickly implement or train rather than other machine learning algorithms. But the drawback of this algorithm is that it is not applicable for complex predictions. So you should know about the different types of machine learning algorithms for getting better results.
In this article, we will learn about GNNs and its structure as well as its applications
Comprehensive walkthrough on using CocoIndex to build unified, incrementally updated search and analytics pipelines.
Privacy](https://hackernoon.com/differential-privacy-with-tensorflow-20-multi-class-text-classification-privacy-yk7a37uh)
Introduction
Here’s the full list of top AI conferences to attend in 2022, from the most technical to business-focused to academic
After noticing my programming courses in college were outdated, I began this year by dropping out of college to teach myself machine learning and artificial intelligence using online resources. With no experience in tech, no previous degrees, here is the degree I designed in Machine Learning and Artificial Intelligence from beginning to end to get me to my goal — to become a well-rounded machine learning and AI engineer.
Learn how to build an NLP model and deploy it with a fast web framework for building APIs called FastAPI.
Machine learning models are often developed in a training environment, which may be online or offline, and can then be deployed to be used with live data once they have been tested.
The question of from-scratch implementation vs Python library comes up once in a while, no matter the goal of your project.
Prices move in a wave like fashion, moving back and forth following a broader trend. While doing so, it often revolves around a mean. It might move across or bounce off the mean. Mean reversion systems are designed to exploit this tendency.
Too lazy to scrape nlp data yourself? In this post, I’ll show you a quick way to scrape NLP datasets using Youtube and Python.
Why average ROI fails. Learn how distributional and tail-risk modeling protects marketing campaigns from catastrophic losses using Bayesian methods.
In machine learning, each type of artificial neural network is tailored to certain tasks. This article will introduce two types of neural networks: convolutional neural networks (CNN) and recurrent neural networks (RNN). Using popular Youtube videos and visual aids, we will explain the difference between CNN and RNN and how they are used in computer vision and natural language processing.
You've most certainly seen movies like the recent Captain Marvel or Gemini Man where Samuel L Jackson and Will Smith appeared to look like they were much younger. This requires hundreds if not thousands of hours of work from professionals manually editing the scenes he appeared in. Instead, you could use a simple AI and do it within a few minutes.
Getting actionable insights around a topic using the new Twitter API v2 endpoint
The article explores Machine Learning's vital role in cybersecurity, addressing evolving digital threats. It covers ML's types, iterative process, feature engi
As part of my data-science career track bootcamp, I had to complete a few personal capstones. For this particular capstone, I opted to focus on building something I personally care about - what better way to learn and possibly build something valuable than by working on a passion project.
I always wanted to learn programming. Writing codes, making algorithms always excited me. Being a mechanical engineer, I was never taught these subjects in depth.
In this article, we cover how to use pipeline patterns in python data engineering projects. Create a functional pipeline, install fastcore, and other steps.
We human beings are depending so much on digital and smart devices. And all these devices are creating data at a very fast rate. According to an article on Forbes more than 90% of the world data has been created in past 2 to 3 years.
With each day, enterprises increasingly rely on data to make decisions.
Comparative Study of Different Adversarial Text to Image Methods
See how Andrei Shcherbinin built production-ready ML systems with 12x faster attribution, 95% chatbot automation, and stronger monitoring.
Another clever tool for a powerful SQL pre-processor
A Must-know for AI Enthusiasts](https://hackernoon.com/diverse-types-of-artificial-intelligence-a-must-know-for-ai-enthusiasts)
A precursory article that explains various categorizations of artificial intelligence, some real-life examples and concepts.
Using Relational Database to search inside unstructured data
This article will serve as a lesson on the shocking reasons for your AI adoption disaster. We see news about machine learning everywhere. Indeed, there is lot of potential in machine learning. According to Gartner’s predictions, “Through 2020, 80% of AI projects will remain alchemy, run by wizards whose talents will not scale in the organization” and Transform 2019 of VentureBeat predicted that 87% of AI projects will never make it into production.
Why you should prepare for BI analyst interview questions?
A beginner’s guide to Probability and Random Events. Understand the key statistics concepts and areas to focus on to ace your next data science interview.
We get a glimpse into the inner workings of a valuable company and it turns out it's not all sunshine and rainbows.
Universal Approximation Theorem says that Feed-Forward Neural Network (also known as Multi-layered Network of Neurons) can act as powerful approximation to learn the non-linear relationship between the input and output. But the problem with the Feed-Forward Neural Network is that the network is prone to over-fitting due to the presence of many parameters within the network to learn.
In this post, I wanted to share a Reddit dataset list that gained a lot of traction on social media when it was first posted.
Google makes billions and billions from paid search, but did you know those folks without adblockers are only responsible for about 6 percent of web visits? Long live free organic search, the primary diet for almost 2/3rds of all web traffic!
You can easily build a personal AI chatbot that runs both online (using OpenAI GPT) and offline (using Ollama local models) right from your local machine.
Handwriting Recognition:
Discover the key differences between MLOps Engineer vs ML Engineer roles, including focus, collaboration, and tooling.
What are context graphs, what are they good for, and why are they dubbed AI’s trillion-dollar opportunity? What does context mean, and how can it be defined?
Learn how to apply a variety of techniques to select features with Xverse package.
I unknowingly blew $3,000 in 6 hours on one query in Google Cloud BigQuery. Here’s why and 3 easy steps to optimize the cost.
Retraining Machine Learning Model, Model Drift, Different ways to identify model drift, Performance Degradation
The hype around AI is growing rapidly, as most research companies predict AI will take on an increasingly important role in the future.
In these difficult days for all of us, I’ve heard all sorts of things. From the fake news sent through Whatsapp, like vitamin C can save your life, to holding your breath in the morning to check if you’ve been hit by COVID-19. The mantra that everyone keeps repeating is “stay at home!”, okay fine, but what exactly does “stay home” mean? The question seems ridiculous when you think of a relatively short period, 15 days? A month? But if we look critically at the situation, we surely realize that it won’t be 15 days, and it won’t be a month. It will be a long, long time. Why am I saying this? Because “stay at home” doesn’t protect us from the virus. Staying at home is to protect our health care facilities from collapse. And I’m not saying that this is wrong. I’m just saying that if we want to protect the health care system from collapse, well then we’ll stay home a long, long time. But in doing so we will irreparably damage the economic system by profoundly changing our social and political model. It is inevitable. Let’s face it and not have too many illusions.
Comprehensive List of Feature Store Architectures for Data Scientists and Big Data Professionals
Here is a list of the best books to learn machine learning for beginners to help build their careers in the ML Industry.
For years AI was touted to be the next big technology. Expected to revolutionize the job industry and effectively kill millions of human jobs, it became the poster child for job cuts. Despite this, its adoption has been increasingly well-received. To the tech experts, this wasn’t really surprising given its vast range of use cases.
Search Engine Optimization (SEO) has been the backbone of an online search for over two decades now. But as Artificial Intelligence (AI) technology moves quickl
Explore the nuances of MySQL’s DATETIME and TIMESTAMP types, from handling time zones and zero dates to optimizing performance and preventing pitfalls.
An article explaining the intuition behind the “positional embedding” in transformer models from the renowned research paper - “Attention Is All You Need”.
Do we need a radical new approach to data warehouse technology? An immutable data warehouse starts with the data consumer SLAs and pipes data in pre-modeled.
Elections play crucial role in all democracies and social media is an important aspect in this process. Presently, political parties increasingly rely on social media platforms like Twitter and Facebook for political communication.The use of social media in political marketing campaigns has grown dramatically over the past few years. It is also expected to become even more critical to future political campaigns, as it creates two-way communication and engagement that stimulates and fosters candidates relationships with their supporters.
Have you ever dreamed of a good transcription tool that would accurately understand what you say and write it down? Not like the automatic YouTube translation tools… I mean, they are good but far from perfect. Just try it out and turn the feature on for the video, and you’ll see what I’m talking about.
Artificial intelligence is changing the world as we know it. Form self-driving cars to weather predictions. Now it's taking on the stock market. Here's how.
Most data science job descriptions are actually for data engineers.
This post includes a round-up of some of the best free beginner tutorials for Machine Learning.
Discover the evolution and importance of decision trees in machine learning, from their early beginnings in the 1960s to their widespread use in modern ensemble
Want to train machine learning models on your Mac’s integrated AMD GPU or an external graphics card? Look no further than PlaidML.
We explore the use of OpenCV and techniques like contour detection for eye blink detection, pupil tracking, Discuss the challenges and their specific Solutions.
We’ve been asked if Airbyte was being built on top of Singer. Even though we loved the initial mission they had, that won’t be the case. Aibyte's data protocol will be compatible with Singer’s, so that you can easily integrate and use Singer’s taps, but our protocol will differ in many ways from theirs.
If you are a beginner and just started machine learning or even an intermediate level programmer, you might have been stuck on how do you solve this problem. Where do you start? and where do you go from here?
This week on HackerNoon's Stories of the Week, we looked at three articles that covered the world of software development from employment to security.
Learn the distinctions between AI and ML with vivid examples.
If you are a two-degree marketplace like Uber, you cater to millions of users requesting a ride through your driver partners accepting and fulfilling those requests. For a three-degree marketplace like Swiggy, there is another static component added (like restaurants or stores), where delivery partners pick up the orders.
A quick demonstration of using JavaScript to download ad hoc data.
This guide explores real-time AI and the unique performance and cost attributes of Cassandra that make it an excellent database for a feature store.
A machine learning guide on how to identify fraudulent credit card transactions by using the PyOD toolkit.
Anscombe’s quartet comprises four data sets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed.
— Wikipedia
Evolution of our data processing architecture towards better performance and simpler maintenance at Tencent Music.
This tutorial will help you get started with NumPy by teaching you to visualize multidimensional arrays.
With the cost of a cup of Starbucks and two hours of your time, you can own your own trained open-source large-scale model.
Running inference at scale is challenging. See how we speed up the I/O performance for large-scale ML/DL offline inference jobs.
Explore the fascinating world of Reinforcement Learning through Multi-Armed Bandits (MABs), balancing exploration & exploitation.
This post covers all you will need for your Journey as a Beginner. All the Resources are provided with links. You just need Time and Your dedication.
Do you know the machine learning global market is estimated to reach $30.6 billion by 2024? This marvellous growth is the outcome of Omni-presence of artificial intelligence and its trending subset; machine learning.
Let’s talk about self-supervised machine learning - a way to teach a model a lot without manual markup, as well as an opportunity to avoid deep learning when setting a model up to solve a problem. This material requires an intermediate level of preparation; there are many references to original publications.
Are you data literate? In today's data-driven world, data literacy is a crucial skill. Here's how you can develop it for yourself.
Use Monte Carlo simulation to understand the risk in fantasy baseball. Learn why optimizing a lineup is a tall order.
DJ Patil and Jeff Hammerbacher coined the title Data Scientist while working at LinkedIn and Facebook, respectively, to mean someone who “uses data to interact with the world, study it and try to come up with new things.”
A beginner level tutorial to get started with data visualization by creating an interesting and intuitive JavaScript bubble map
On November 15th, MetaAI and Papers with Code announced the release of Galactica, a game-changer, open-source large language model trained on scientific knowledge with 120 billion parameters.
From astrophysics to data science, here's a story of a lifetime journey with modeling the Universe and other dynamic things that move through space and time.
Data analysis as a whole is one of the most important industries. Now that DeFi is a full-fledged industry, there is a growing need for valuable data analytics.
Find the top 40+ product interview questions you must prepare for your next data science interview.
How Jupyter Notebooks played an important role in the incredible rise in popularity of Data Science and why they are its future.
Learn how you can use real-time data in digital marketing for customer engagement and retention, analyze real-time data for faster decision-making
A large portion of mild and asymptomatic cases may go unreported. The data will never be perfect, the true cases are likely much larger as the testing frequency and effectiveness vary in different regions.
To learn about SQL, we need to understand how a DBMS works. DBMS or Database Management System is essentially a software to create and manage databases.
Multi-output Machine Learning — MixedRandomForest
Only 38% of the passengers survived this devastating event, prompting me to wonder about the individuals who were aboard the Titanic that fateful night.
This article explains how I found a nice and simple algorithm to extract prominent colors out of an image.
A cold start problem is when the system cannot draw any inferences for users or items about which it has not yet gathered sufficient information. Simply put, if you have no or less initial data, what recommendation is the system supposed to give to the user?
While recommender systems are useful for users who have some previous interaction history, the same might not be the case for a new user or a newly added item. The problem is that in both cases we don’t have any history to base the recommendations on.
Computer vision technology is the poster child of artificial intelligence. It is the sector of the industry that gets the most media attention because of the tools and benefits the technology can provide. From autonomous vehicles and drones to cancer detection and augmented reality, technologies that once only existed in science fiction are now at our doorstep.
EDA for Data Analysis or Data Visualization is very important. It gives a brief summary and main characteristics of data. According to a survey, Data Scientist uses their most of time to perform EDA tasks.
Learn how to build an AI chat bot for your own data within 40 minutes. An end-to-end LLM tutorial.
Explore the pros and cons of industry-leading blockchain analytic tools, examining how each solution handles data across the blockchain network.
Dataism suggests that the entire universe can be interpreted as data flows and that all phenomena, including human behaviour, can be reduced to data processes.
A modern business user’s relationship with data is fairly complicated. It starts with curiosity. “Which of my top users will do X,Y, or Z?” You need a data output to move forward with a decision—except you’re having communication issues.
In this article, I look into some of the shortages of event-driven
programming and suggest behavior trees as an effective alternative,
suitable for back/front-end application development.
Discover how data science is revolutionizing cyber security and learn about its role in detecting and preventing cyber-attacks.
Get to know Council, ChainML's open-source, AI agent platform.
Data bias in machine learning is a type of error in which certain elements of a dataset are more heavily weighted and/or represented than others. A biased dataset does not accurately represent a model’s use case, resulting in skewed outcomes, low accuracy levels, and analytical errors.
Although sequential data is pretty common to be found and highly useful, there are many reasons that lead to not leverage it
Experimentation designing in the marketplace without AB-Testing using Synthetic Control Groups and Switchbacks.
What’s the Role of Data Science in Finance?
This guide isn’t just a compilation of LLM resources; it's a curated journey through the most valuable skills in the industry.
Unveiling Docker's Potential in Modern IT Landscapes - An In-Depth Exploration of Applications and Best Practices.
If you’re not already using low-code platforms, you will be very soon. Low-code is helping to significantly speed up timelines, while bringing down costs
Apparently hot-cold data separation is hot now. Let's figure out why.
Data augmentation is a technique used by practitioners to increase the data by creating modified data from the existing data.
After reading this, you'll likely have a whole new perspective on how AIGC enhances development efficiency.
Discover how Large Language Models face prompt manipulation, paving the way for malicious intent, and explore defense strategies against these attacks.
A prelusive article comprehending the concept behind model calibration, its importance and usage in machine learning model development.
In 2012, Harvard Business Review called data scientists the sexiest job of the 21st century. However, correctly answering data science interview questions to get a job as a data scientist is very tricky.
The young field of AI Safety is still in the process of identifying its challenges and limitations. In this paper, we formally describe one such impossibility result, namely Unpredictability of AI. We prove that it is impossible to precisely and consistently predict what specific actions a smarter-than-human intelligent system will take to achieve its objectives, even if we know terminal goals of the system. In conclusion, impact of Unpredictability on AI Safety is discussed.
This article will discuss how data lineage can help in user data governance and explore how serverless technology can be incorporated to achieve better results.
KNIME Analytics is a data science environment written in Java and built on Eclipse. This software allows visual programming for data science applications.
The core design philosophy of SeaTunnel CDC is to find the perfect balance between "Fast" (parallel snapshots) and "Stable" (data consistency).
At HackerNoon, we pride ourselves on supporting startups because we know how hard it can be to start and run a company.
From the most popular seats to the most popular viewing times, we wanted to find out more about the movie trends in Singapore . So we created PopcornData — a website to get a glimpse of Singapore’s Movie trends — by scraping data, finding interesting insights, and visualizing them.
Subscribe to these Machine Learning YouTube channels today for AI, ML, and computer science tutorial videos.
BlobGAN allows for unreal manipulation of images, made super easily controlling simple blobs. All these small blobs represent an object, and you can move them around or make them bigger, smaller, or even remove them, and it will have the same effect on the object it represents in the image. This is so cool!
Using the new Tableau version 2020.1 onwards.
Learn why data could become the most promising NFT utility that sets the foundation for a valuable trend: Data Finance (DataFi).
Although the internet made a lot of things easier for the insurance companies, there were still many pain points left to be addressed.
Most of us in data science have seen a lot of AI-generated people in recent times, whether it be in papers, blogs, or videos. We’ve reached a stage where it’s becoming increasingly difficult to distinguish between actual human faces and faces generated by artificial intelligence. However, with the current available machine learning toolkits, creating these images yourself is not as difficult as you might think.
The new PULSE: Photo Upsampling algorithm transforms a blurry image into a high-resolution image.
Incorporating AI into fitness apps can still be confusing, especially if you haven’t worked with AI before. Learn how AI can be applied to the fitness industry.
Get a primer on PostgreSQL aggregation, how PostgreSQL´s implementation inspired us as we built TimescaleDB hyperfunctions and what it means for developers.
List of Top 10 Data Scientist skills that guaranteed employment. As well as a selection of helpful resources to master these skills
Check out these 7 amazing open source projects that every data scientist /analyst should know about. These tools can make your life so much easier.
How can Uber deliver food and always arrive on time or a few minutes before? How do they match riders to drivers so that you can always find a Uber? All that while also managing all the drivers?!
Here's a compilation of some of the best + free machine learning courses available online.
One of the most popular apps of 2019, TikTok ruled the download charts in both the Android and Apple markets. Having more than 1.5 billion downloads and approximately half a billion monthly active users, TikTok definitely has access to a trove of users. With that large user base comes a hidden goldmine: their data.
A data-driven intro to proxies in the context of web scraping.
Build best automated AI chat bot using Google Dialog flow
Graphs, and knowledge graphs, are key concepts and technologies for the 2020s. What will they look like, and what will they enable going forward?
As technology penetrates every facet of life, and continues to grow exponentially, the solution potential becomes enormous. At the same time, we're in a world where billions live in poverty, and millions are on the brink of famine. In order to support an ever-growing populace, we need to leave no stone un-turned in the search for solutions. AI provides many potential solutions to humanity's greatest challenges."AI" is a vague, even confusing term. If you hear the phrase "artificial intelligence," you might wonder why there aren't sentient robots walking around, or why everyone isn't in self-driving cars already. The reality is that "AI" is just a marketing term for a set of computational statistical tools, or more simply, algorithms.However, as versatile as mathematics is, so is AI. AI is limited by (primarily) a couple things: data and computational power. Both the data and the compute power we have available are growing exponentially, so AI is becoming more and more powerful.With this increase in data and computational ability, AI is now being used in a wide variety of applications.For example, bitgrit (disclaimer: I'm CEO), collects meaningful AI problem statements to crowd-source solutions to data scientists. Some problem statements include saving animals’ lives, increasing agricultural yield, and speeding up healthcare claims processing.Michael Suttles, CEO at Save All The Pets, explains how data and AI can be used to save shelter animals:
Each time a new business ecosystem forms, we have to ask a simple question: where's value created?
Learn About SVG for Data Visualization, to make Complex Information Clear and Beautiful.
This blog is part 1 of (and contains a link to) a 70+ page report was created to quickly find data resources and/or assets for a given dataset and a specific ta
Data visualisation infographic with insights on salary level of data scientists - how to create the JavaScript dashboard and analyse its data
Prior to analyzing large chunks of data, enterprises must homogenize them in a way that makes them available and accessible to decision-makers. Presently, data comes from many sources, and every particular source can define similar data points in different ways. Say for example, the state field in a source system may exhibit “Illinois” but the destination keeps it is as “IL”.
A software engineer’s journey into data science at Yelp and Uber
Today, misconceptions about AI are spreading like wildfire.
As an online retailer, how can you improve your business? Of course through providing a better customer experience. An e-commerce company needs to have a well understanding of the following factors:
In machine learning, hot topics such as autonomous vehicles, GANs, and face recognition often take up most of the media spotlight. However, another equally important issue that data scientists are working to solve is anomaly detection. From network security to financial fraud, anomaly detection helps protect businesses, individuals, and online communities. To help improve anomaly detection, researchers have developed a new approach called MIDAS.
Meta Article with links to all the interviews with my Machine Learning Heroes: Practitioners, Researchers and Kagglers.
Hard links and symbolic links have been available since time immemorial, and we use them all the time without even thinking about it. In machine learning projects they can help us, when setting up new experiments, to rearrange data files quickly and efficiently in machine learning projects. However, with traditional links, we run the risk of polluting the data files with erroneous edits. In this blog post we’ll go over the details of using links, some cool new stuff in modern file systems (reflinks), and an example of how DVC (Data Version Control, https://dvc.org/) leverages this.
Adopting AI in our data analytic solution is a bumpy journey, but phew, it now works well for us.
Learn how to update your Rstudio open source software and why you should keep it up to date.
In this article, we would be analyzing data related to US road accidents, which can be utilized to study accident-prone locations and influential factors.
Rio is a brand new GUI framework designed to let you create modern web apps with just a few lines of Python. Our goal is to simplify web and app development.
Why is it that, for most organizations, building successful AI applications is a huge challenge? It can be boiled down to three big hurdles.
It is easy to be annoyed by strange anomalies when they are sighted within otherwise clean (or perhaps not-quite-so-clean) datasets. This annoyance is immediately followed by eagerness to filter them out and move on. Even though having clean, well-curated datasets is an important step in the process of creating robust models, one should resist the urge to purge all anomalies immediately — in doing so, there is a real risk of throwing away valuable insights that could lead to significant improvements in your models, products, or even business processes.
Centralized crypto exchanges are the most important black box of the crypto ecosystem. We all use them, we have a love-hate relationship with them, and we understand very little about their internal behavior. At IntoTheBlock, we have been heads down working on a series of machine learning models that help us better understand the internal of crypto exchanges. Recently, we presented some of our initial findings at a highly oversubscribed webinar and I thought it would be elaborate further in some of the ideas discussed there.
Data Science is no doubt the "sexiest" career path of the 21st century, made up of people with strong intellectual curiosity and technical expertise to dig out valuable insights from humongous volumes of data. This helps firms add value by improving their productivity, unlocking insights for better decision making and profit gains, just to mention a few. The knowledge of Data Science is desirable and useful across various industries.
Introducing a customizable and interactable Decision Tree-Framework written in Python
When asked what advice he'd give to world leaders, Elon Musk replied, "Implement a protocol to control the development of Artificial Intelligence."
Is there a programming language that's good for every user from age 8 to 80? You bet! It's called Smalltalk.
A primer to understand how technology is poised to disrupt law
In this guide, we’ll show the must know Python libraries for machine learning and data science.
Data science came a long way from the early days of Knowledge Discovery in Databases (KDD) and Very Large Data Bases (VLDB) conferences.
Learn how counterfactual forecasting helps data scientists measure true revenue impact by simulating causal scenarios beyond traditional time series models.
Propensity model to figure out the likelihood of a person buying a product on their return visit.
We need to identify the probability to convert for each user.
Natural language processing (NLP) is one of the biggest fields of AI development. Numerous NLP solutions like chatbots, automatic speech recognition, and sentiment analysis programs can improve efficiency and productivity in various businesses around the world.
Discover 15 essential Python libraries for data science & machine learning, covering data mining, visualization & processing.
Master creating interactive point maps in JavaScript! Step-by-step guide using millionaire counts for global cities for illustration. Dive in now!
As posited by Lev Tolstoy in his seminal work, Anna Karenina: “Happy families are all alike; every unhappy family is unhappy in its own way.” Likewise, all successful data science projects go through a very similar building process, while there are tons of different ways to fail a data science project. However, I’ve decided to prepare a detailed guide aimed at data scientists who want to make sure that their project will be a 100% disaster.
Data science projects are focusing on solving social or business problems by using data. Solving data science projects can be a very challenging task for beginners in this field. You will need to have a different skills set depending on the type of data problem you want to solve.
Here at TimeNet, we’re building a large time series database with the primary aim of benefitting society through access to data. In this post we’ll study different time series representing both the true, and the perceived spread of the coronavirus (COVID-19) pandemic. Daily COVID-19 numbers are currently available on TimeNet.cloud for many countries. We’re expanding these datasets with further variables measuring how we (people) perceive the significance of the pandemic. We use stock market movements and internet search trends to quantify the virus’s perceived spread.
In this article, we will take a look at each one of the machine learning tools offered by AWS and understand the type of problems they try to solve for their customers.
Let’s talk about the one and only project you need to build, that’ll help you gain fullstack data science experience, and impress interviewers on your interviews if your goal is to jumpstart your career in data science.
Data Science and ML have become competitive differentiator for organizations across industries. But a large number of ML models fail to go into production. Why?
In this article, I will show you how to migrate data from S3 to Snowball.
Becoming a health data scientist can be challenging but rewarding; it merges statistical analysis with other tools to gain insights from healthcare data.
How to access medical data in DICOM format (MR, CT, X-Ray) from Python
Maximizing efficiency is about knowing how the data science puzzles fit together and then executing them.
Podcasts have unequivocally become one of the most dominant forms of media consumption in recent years.
Some users need to migrate their scheduling system from Airflow to Apache Dolphinscheduler, this is a guide to transfer from Airflow to DolphinScheduler.
Meet The Entrepreneur: Alon Lev, CEO, Qwak
Since the dawn of time, humans have communicated through gestures, drawings, smoke, or speech. Along the way, Structured Query Language (SQL) made its way into human life so we could speak to databases. However, it’s time to revert back to our natural language and rethink how we talk to our data.
A list of African language datasets from across the web that can be used in numerous NLP tasks.
At the heart of Machine Learning is to process data. Your machine learning tools are as good as the quality of your data. This blog deals with the various steps of cleaning data. Your data needs to go through a few steps before it is could be used for making predictions.
Best practices and things I’ve learned along the way.
With the exponential rise in applications of AI, Data Science, and Machine Learning these are the critical Ethical AI Libraries to know.
Data trust starts and ends with communication. Here’s how best-in-class data teams are certifying tables as approved for use across their organization.
Swahili (also known as Kiswahili) is one of the most spoken languages in Africa. It is spoken by 100–150 million people across East Africa. Swahili is popularly used as a second language by people across the African continent and taught in schools and universities. In Tanzania, it is one of two national languages (the other is English).
Pytorch is a powerful open-source deep-learning framework that is quickly gaining popularity among researchers and developers
Deep dive into why model.fit beats naive normal equations in linear regression, with SVD, conditioning and floating-point pitfalls explained.
Product teams have a lot of great practices that data teams would benefit from adopting. Namely: user-centricity and proactivity.
Introducing PeerVest: A free ML app to help you pick the best loan pool on a risk-reward basis
SVM works by finding a hyperplane in an N-dimensional space (N number of features) which fits to the multidimensional data while considering a margin.
Simplicity is the best policy.
Introduction
So You Want to Get Into Data Science
Large-scale users of Cassandra, like Uber and Apple, exemplify how this database system can effectively lower the risk in AI/ML projects.
Machine Learning is a rapidly growing and very complex field of study. Generative Models might prove to be a new breakthrough that will make a new boom.
Looking to make your data scientist resume more attractive to employers?
Explore boosting trees' evolution: from AdaBoost to XGBoost, LightGBM, and CatBoost. Learn key updates & how to choose the right library for your needs.
Law enforcement agencies are not new to the data and its usage, but with the advancement in technology, Data science in law enforcement has become a need.
Levels of Annotation Automation
Machine Learning Operations (MLOps) is a form of DevOps in a growing area. In this article, we'll discuss the top 5 Machine Learning Platforms to watch in 2022.
Panoptic scene graph generation, or PSG, is a new problem task aiming to generate a more comprehensive graph representation of an image or scene based on panoptic segmentation rather than bounding boxes. It can be used to understand images and generate sentences describing what's happening. This may be the most challenging task for an AI! Learn more in the video…
These books cover the Introductory level to Expert level of knowledge and concepts in ML. These Books have some core factors about ML. Give them a try. Lets Start.
Building apps with unreal levels of personalized context has become a reality for anyone who has the right database, a few lines of code, and an LLM like GPT-4.
What has changed in the software world to elevate the importance of exposing databases as APIs?
Explore how exactly distributed storage works in Hadoop? We have to characterize an essential node (known as NameNode) from one of the workers (DataNodes).
Re-boot of “Interview with Machine Learning Heroes” and collection of best pieces of advice
The relationship between Bitcoin and Gold is one of the dynamics that seems to constantly capture the minds of financial analysts. Recently, there have been a series of new articles claiming an increasing “correlation” between Bitcoin and Gold and the phenomenon seems to be constantly debated in financial media outlets like CNBC or Bloomberg.
An interview with Petar Veličković, research scientist at Google Deepmind, The What's AI Podcast episode 17!
Data Version Control (DVC) is a data-focused version of Git. In fact, it’s almost exactly like Git in terms of features and workflows associated with it.
In this blog, I will be sharing my learnings and experience from one of the deployed models.
Information Technology (IT) certification can enrich your IT career and pave the way for a profitable way. As the demand for IT professionals increases, let's look at 10 high-paying certifications. The technology landscape is constantly changing and the demand for information technology certification is also getting higher. Popular areas of IT include networking, cloud computing, project management, and security. Eighty percent of IT professionals say certification is useful for careers and the challenge is to identify areas of interest. Let's take a look at the certifications that are most needed and the salaries that correspond to them.
Transformer-based models are a game-changer when it comes to using unstructured text data. As of September 2020, the top-performing models in the General Language Understanding Evaluation (GLUE) benchmark are all BERT transformer-based models. At Georgian, we often encounter scenarios where we have supporting tabular feature information and unstructured text data. We found that by using the tabular data in these models, we could further improve performance, so we set out to build a toolkit that makes it easier for others to do the same.
Bridging the gap between Application Developers and Data Scientists, the demand for Data Engineers rose up to 50% in 2020, especially due to increase in investments in AI-based SaaS products.
Up until recently, we accepted the “black box” narrative surrounding AI as a necessary evil that could not be extrapolated away from AI as a concept.
HackerNoon's Startups of The Year voting ends March 31, 2025. You still have time to support your favorites—cast your vote today!
How to use Machine learning, Deep learning and Computer Vision for building Optical Character Recognition (OCR) solution for text recognition.
The Weighted Random algorithm is used to send HTTP requests to Nginx servers. In this article, you'll learn how the Weighted Random algorithm works.
Scikit-learn is the most popular open-source and free python machine learning library for Data scientists and Machine learning practitioners. The scikit-learn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.
What if you could instantly visualize the political affiliation of an entire city, down to every single apartment and human registered to vote? Somewhat surprisingly, the City of New York made this a reality in early 2019, when the NYC Board of Elections decided to release 4.6 million voter records online, as reported by the New York Times. These records included full name, home address, political affiliation, and whether you have registered in the past 2 years. The reason according to this article was:
Understanding MySQL explains query output is essential to optimize the query. EXPLAIN is good tool to analyze your query.
In this article I'm going to make a case why people serious about creating machine learning algorithms and high performance data science programming should use Julia rather than Python.
Unsurprisingly, the data that our apps have collected about us is both impressive and concerning, though it can be very interesting to review and explore it.
Writing ML code as pipelines from the get-go reduces technical debt and increases velocity of getting ML in production.
Machine learning (ML) is a technology or field of computer science that learns from historical data to make accurate predictions or decisions.
The requirement for its stockpiling also grew as the world entered the period of huge information. The principle focal point of endeavors was on structure framework and answers for store information. When frameworks like Hadoop tackled the issue of capacity, preparing of this information turned into a challenge. Data science began assuming a crucial job to take care of this issue. Information Science is the fate of Artificial Intelligence as It can increase the value of your business.
Location-based information makes the field of geospatial analytics so popular today. Collecting useful data requires some unique tools covered in this blog.
The global knowledge graph market is projected to reach $6.93 billion by 2030.
Historically, technological revolutions have triggered similar waves of anxiety, only for the long-term outcomes to demonstrate a more optimistic narrative.
I. Benchmark, benchmark, benchmark
Public web data unlocks many opportunities for businesses that can harness it. Here’s how to prepare for working with this type of data.
A world where the future of humanity can be predicted through an interdisciplinary science called psychohistory! A data scientist's review of Foundation Series.
“Is it possible for a technology solution to replace fitness coaches? Well, someone still has to motivate you saying “Come On, even my grandma can do better!” But from a technology point of view, this high-level requirement led us to 3D human pose estimation technology.
In this tutorial, we are going to create our own e-commerce search API with support for both eBay and Etsy without using any external APIs.
Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand.
Back in 2016, Glassdoor declared that being a Data Scientist was the best job in America.
Our online feeds are broken and large language models have the power to fix them.
Year of the Graph Newsletter, September 2019
Long recognized as a must in the data-driven world, data governance has never been easy for big and tiny organizations alike.
What is hierarchical clustering in unsupervised learning?
SUS scale and why you should try to use it in your UX research.
Microservices can be made using lightweight PHP frameworks like Slim or Lumen, providing minimalistic setups that allow developers focus on core functionalities
Part of building a profitable trading strategy is quickly testing novel ideas. These tend to be the money makers in the rare case that they prove useful once you can integrate them into your strategy.
While HBR declared "Data Scientist" the sexiest job of the 21st century, let's admit that the prevailing view is that it's a geeky, highly-technical field.
Here are the five best articles related to artificial intelligence in February, hoping they will make you want to learn more and visit their website.
The main metric for educational product is it's completition rate. To improve it, one can use the principles of motivational design.
Exploratory data analysis is one of the most important parts of any machine learning workflow and Natural Language Processing is no different.
Facial recognition, is one of the largest areas of research within computer vision. This article will introduce 5 face recognition papers for data scientists.
In this tutorial, I’ll show you more sophisticated data access tools that are more like a surgical scalpel.
With the enormity of data, data visualization has become the most sought-after method to depict huge numbers in simpler versions of maps or graphs.
One of Bitcoin’s strengths and the thing that makes it unique in the finance world is its radical transparency. Blockchain data is like a window, you can see right through it.
Stablecoins are one of the most relevant developments in the crypto ecosystem and one that has been increasingly getting traction. Recently, I presented a session that highlighted some interesting analyses that arise from applying data science methods on stablecoin’s blockchain data. The slide deck and video from the session will be available soon but I thought I share some of the most intriguing data points.
From a technical architecture perspective, I believe this wave of AI will profoundly reshape the entire software ecosystem.
The recipe for successful A/B testing is quick computation, no duplication, and no data loss. So, we used Apache Flink and Doris to build our data platform.
See how to leverage the Airflow ShortCircuitOperator to create data circuit breakers to prevent bad data from reaching your data pipelines.
Applying machine learning models at scale in production can be hard. Here's the four biggest challenges data teams face and how to solve them.
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