The Best Articles in Machine Learning
The most useful articles in Machine Learning from around the web—beginners to advanced—curated by thought leaders and our community. We focus on timeless pieces and update the list whenever we discover new, must-read articles or videos—make sure to bookmark and revisit this page.
Top 5 Machine Learning Articles
At a glance: these are the articles that have been most read, shared, and saved in Machine Learning by Refind users in 2023 so far.
- What Is ChatGPT Doing … and Why Does It Work?
- Machine learning could vastly speed up the search for new metals
- Free AI for Beginners Course
- Why Meta’s latest large language model only survived three days online
- Intercom on Product: How ChatGPT changed everything
Watch a video to get a quick overview.
How to write good AI prompts
(Getting started with Notion AI 4 of 5) Learn how to generate text from scratch using Notion AI, with a focus on writing good prompts and refining output.Get...
What is ...?
New to Machine Learning? These articles make an excellent introduction.
Free AI for Beginners Course
Microsoft has put together an AI course for beginners, consisting of a 12 week, 24 lesson curriculum, available for free to all.
What is reinforcement learning? How AI trains itself
Reinforcement learning is the subset of ML by which an algorithm can be programmed to respond to complex environments for optimal results.
Learn Julia For Beginners – The Future Programming Language of Data Science and Machine Learning Explained
Julia is a high-level, dynamic programming language, designed to give users the speed of C/C++ while remaining as easy to use as Python. This means that developers can solve problems faster and more…
«So now the function is defined to take in only a string. Let us test this out to make sure we can only call the function with a string value»
What is it to be Bayesian? The (pretty simple) math modelling behind a Big Data buzzword
If you’ve ever tripped up over the term ‘Bayesian’ while reading up on data or tech, fear not. Strip away the jargon and notation, and even the mathematics-averse can make sense of the simple yet…
Introduction to Machine Learning for Developers
Developers often hear about leveraging machine learning algorithms in order to build more intelligent applications, but many don’t know where to start.
How to ...?
How to Spot a Machine Learning Opportunity, Even If You Aren’t a Data Scientist
What do you want to predict, and do you have the data?
«What are some outcomes worth guessing? And do we have the data necessary to do supervised learning?»
How to learn PyTorch: A resources guide for developers
If you want to learn PyTorch, check out these books, courses, tutorials, videos, and websites about the open source machine learning library.
These links are currently making the rounds in Machine Learning on Refind.
Google "We Have No Moat, And Neither Does OpenAI"
Leaked Internal Google Document Claims Open Source AI Will Outcompete Google and OpenAI
A Beginner's Guide to Prompt Engineering with GitHub Copilot
When I started using GitHub Copilot and other generative AI tools, I felt frustrated because I wasn't receiving the expected results. How were people feeling so successful with these tools, and why…
Some Neural Networks Learn Language Like Humans
Researchers uncover striking parallels in the ways that humans and machine learning models acquire language skills.
All the Hard Stuff Nobody Talks About when Building Products with LLMs
In this post, Phillip talks through the challenges & pitfalls of LLMs we faced when building our Query Assistant - and that you too may face.
Programming with Natural Language Is Actually Going to Work—Stephen Wolfram Writings
Articles by Stephen Wolfram covering artificial intelligence, computational science and computational thinking, data science, education, future and historical perspectives, sciences, software design,…
Short on time? Check out these useful short articles in Machine Learning—all under 10 minutes.
Researchers Discover a More Flexible Approach to Machine Learning
“Liquid” neural nets, based on a worm’s nervous system, can transform their underlying algorithms on the fly, giving them unprecedented speed and adaptability.
GPT-4 is bigger and better than ChatGPT—but OpenAI won’t say why
We got a first look at the much-anticipated big new language model from OpenAI. But this time how it works is even more under wraps.
Why Meta’s latest large language model only survived three days online
Galactica was supposed to help scientists. Instead, it mindlessly spat out biased and incorrect nonsense.
Do you actually need a vector database?
Spoiler alert: the answer is maybe! Although, my inclusion of the word “actually” betrays my bias. Vector databases are having their day right now. Three different vector DB companies have raised…
Machine learning could vastly speed up the search for new metals
It’s a development that could be useful for applications from outer space to the deep sea.
These are some of the most-read long-form articles in Machine Learning.
What Is ChatGPT Doing … and Why Does It Work?
Stephen Wolfram explores the broader picture of what's going on inside ChatGPT and why it produces meaningful text. Discusses models, training neural nets, embeddings, tokens, transformers, language…
Prompt Engineering vs. Blind Prompting
Shared by 597, including Nico Müller 🇺🇦, Matt Schlicht, Alexander Seifert
The Waluigi Effect (mega-post)
Everyone carries a shadow, and the less it is embodied in the individual’s conscious life, the blacker and denser it is. — Carl Jung …
Intercom on Product: How ChatGPT changed everything
Listen to our Co-founder Des Traynor and Director of Machine Learning Fergal Reid discuss the implications of ChatGPT on tech and customer support.
Large Language Model: world models or surface statistics?
A mystery Large Language Models (LLM) are on fire, capturing public attention by their ability to provide seemingly impressive completions to user prompts (NYT coverage). They are a delicate…
We monitor hundreds of thought leaders, influencers, and newsletters in Machine Learning, including:
Co-Founder of Coursera; Stanford CS adjunct faculty. Former head of Baidu AI Group/Google Brain. #ai #machinelearning, #deeplearning #MOOCs
Co-Founder of @HiddenDoorCo. Formerly Founder of @FastForwardLabs (acquired by @Cloudera). I ♥ data and cheeseburgers. She/her.
Co-founder & CEO Google DeepMind - working on AGI. Trying to understand the fundamental nature of reality. Also revolutionising drug discovery @IsomorphicLabs
The Gradient (sigmoid.social/@thegradient)
The Gradient cuts through the hype and the cynicism to provide accessible, sophisticated reporting on the latest AI research.
Kirk Borne #AlteryxInspire
Advisor to startups. Freelancer. Global Speaker. Founder @LeadershipData. Top influencer in #BigData #DataScience #AI #IoT #ML #B2B. PhD Astrophysics @Caltech
We monitor hundreds of publications, blogs, newsletters, and news sources in Machine Learning, including:
MIT Technology Review
Our in-depth reporting on innovation reveals and explains what’s really happening now to help you know what’s coming next. http://technologyreview.com/newsletters
Big ideas in science and math. Because you want to know more. Launched by @SimonsFdn. 2022 Pulitzer Prize in Explanatory Reporting. http://quantamagazine.org
KD stands for Knowledge Discovery. Covering #DataScience #MachineLearning #AI #Analytics. Edited by @mattmayo13. Founded by Gregory Piatetsky-Shapiro.
MIT Sloan School of Management
Ideas made to matter.
Massachusetts Institute of Technology (MIT)
The Massachusetts Institute of Technology is a world leader in research and education. Related accounts: @MITevents @MITstudents @MIT_alumni
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How many sources does Refind monitor?
We monitor 10k+ content sources on hundreds of topics—publications, blogs, news sites, newsletters, Substack, Medium, Twitter, etc.
Which sources does Refind monitor in Machine Learning?
We monitor hundreds of sources in Machine Learning, including MIT Technology Review, Quanta Magazine, KDnuggets, MIT Sloan School of Management, Massachusetts Institute of Technology (MIT), and many more.
Who are the thought leaders in Machine Learning?
We follow dozens of thought leaders in Machine Learning, including Andrew Ng, Hilary Mason, Demis Hassabis, The Gradient (sigmoid.social/@thegradient), Kirk Borne #AlteryxInspire.
Missing a thought leader? Submit them here
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