16 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
The personal site of François Chollet, software engineer and AI researcher, co-founder of Ndea and the ARC Prize. It collects his books (including Deep Learning with Python), software, papers, essays and talks, and describes his research interests: the nature of abstraction, autonomous abstraction algorithms, and democratizing AI development.
Jürgen Schmidhuber's technical report (IDSIA-22-22, arXiv:2212.11279) tracing the history of machine learning and deep neural networks, with his annotations on who contributed what.
OpenAI's free educational resource for learning deep reinforcement learning: an introduction to the field, key algorithms with implementations, exercises, and guidance for becoming an RL practitioner or researcher.
Essay arguing that scaling up neural networks — more data, compute and parameters — may be sufficient to reach general intelligence, without fundamentally new algorithms. Written by researcher and essayist Gwern Branwen.
The most widely used open-source framework for building and training AI models, with free tutorials, documentation and pre-trained models. Most AI research papers today are built on it.
Why I recommend it: Free and open source, now governed by the Linux Foundation's PyTorch Foundation — but Meta created it and remains its biggest contributor, so its direction still reflects big-tech priorities.
Google's open-source framework for building and training AI models, with free guides and pre-trained models. Once the industry standard, it's now used less in new research than PyTorch but still powers many production systems.
Why I recommend it: Free and open source, but it's a Google project — its development priorities follow Google's. For a new learner, PyTorch is the more common starting point in 2026.
Deep learning pioneer and Turing Award winner, now focused on AI risk. His site holds papers, talks and written positions; his Google Scholar list has the full publication record, most-cited first.
From the site: Yoshua Bengio is Full Professor of Computer Science at Université de Montreal, Co-President and Scientific Director of LawZero, as well as the Founder and Scientific Advisor of Mila. He also holds a Canada CIFAR AI Chair.
Why I recommend it: One of the three people whose work made modern AI possible, who now spends much of his time arguing it needs guardrails. Read him alongside people who disagree.
Personal site of Andrew Ng, co-founder of Coursera and DeepLearning.AI and a founding lead of Google Brain, collecting his courses, writing and current projects.
From the site: Andrew Ng has helped millions of people learn AI. Founder of DeepLearning.AI, AI Fund, and LandingAI. Co-Founder of Coursera. Board Director at Amazon.
Why I recommend it: His teaching is the cheapest serious on-ramp into machine learning that exists. Start from the courses list rather than the news.
Department site for one of the birthplaces of modern deep learning, with faculty pages, open research groups and course listings.
From the site: The University of Toronto
Why I recommend it: Where Hinton's group worked. Useful for finding the original papers and the people still there, rather than for courses you can enrol in.
Google Scholar profile listing Geoffrey Hinton's papers in citation order — backpropagation, dropout, AlexNet, t-SNE and the rest of the deep learning canon.
From the site: Emeritus Prof. Computer Science, University of Toronto - Cited by 1.089.325 - machine learning - psychology - artificial intelligence - cognitive science - computer science
Why I recommend it: The single best index of the papers that made current AI work. Sort by year to see the ideas arrive. Scholar blocks automated visits, so the picture may be a screenshot.
A free four-month, hands-on machine learning engineering course covering Python, regression and classification, XGBoost, deep learning with TensorFlow, Docker, Kubernetes and cloud deployment, with homework, projects and a Slack community. The 2026 cohort started September 14, 2026, and you can still start now.
Why I recommend it: One of the strongest free routes into machine learning work, because you finish with deployed projects, not just notes.
Curated weekly newsletter covering AI research, tools, industry moves and practical applications.
Why I recommend it: A readable weekly roundup for staying current without drowning in AI news.
Official TensorFlow tutorials covering machine-learning fundamentals, computer vision, NLP, and generative AI workflows.
Why I recommend it: A reliable, free path into applied AI if you are ready to move past the headline demos and start building.
Top-down deep learning course using PyTorch, covering computer vision, NLP, and model deployment. Assumes existing Python basics.
Why I recommend it: Teaches you to build working models in lesson one instead of six weeks of math first. Best fit if you already know some Python.
Five-course series on neural networks, convolutional and sequence models, and how to structure ML projects.
Why I recommend it: The natural follow-on to the Machine Learning Specialization, not a starting point. Audit it free and build one project you can explain end to end.
NVIDIA's catalog filtered to its free self-paced courses covering AI, deep learning, generative AI, data science, accelerated computing, and CUDA — with certificates of competency on many tracks.
In plain terms: This website offers a collection of free, self-paced online classes in artificial intelligence, data science, and computing. You can take lessons to build new technical skills and earn certificates to share with employers.
From the site: Browse NVIDIA self-paced and instructor-led training, including free courses in AI, deep learning, generative AI, data science, and accelerated computing.
Why I recommend it: Start with a free intro course and put it on your resume under a "Continued Learning" section. Hiring managers notice vendor-name training, and NVIDIA carries weight in AI and data roles.
