Learning and research links

A collection of resources for learning machine learning, understanding research papers, and putting ideas into practice. For reading paths through this blog, see the topic guide. My ML Dojo repositories collect additional learning material and code.

A good starting point: choose fast.ai for a practical introduction if you already write code, or Michael Nielsen's Neural Networks and Deep Learning for a gradual explanation of the fundamentals.

Courses · Books · Research and explanations · Tools and datasets · Communities · Historical material

Courses🔗

• Practical Deep Learning for Coders: fast.ai's project-led course for people with programming experience. It introduces working models before unpacking how they learn; you do not need to own a GPU to begin.
• Kaggle Learn: short, exercise-based introductions to Python, data handling, and machine learning.
• Harvard CS109: Data Science: data analysis, statistical modelling, and machine learning. The course site links to the introductory and advanced courses and earlier editions.
• Stanford CS221: Artificial Intelligence: a broader foundation in AI, including search, reasoning, learning, and decision-making. This link points to the Spring 2026 course.
• Stanford CS229: Machine Learning: a mathematical treatment of learning algorithms. Best approached with some linear algebra, probability, and calculus.
• Stanford CS231n: Deep Learning for Computer Vision: neural-network methods for visual recognition. The course notes are also useful as a standalone reference.
• Stanford CS224n: Natural Language Processing with Deep Learning: representations, neural models, and learning methods for language.
• Berkeley CS188: Introduction to Artificial Intelligence: core AI ideas with programming projects.
• Berkeley CS285: Deep Reinforcement Learning: an advanced course on learning to act, including policy optimisation and model-based methods.

Books🔗

• Neural Networks and Deep Learning, Michael Nielsen: an online introduction that develops neural networks, backpropagation, and learning from examples step by step.
• Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville: a substantial reference on mathematical foundations and neural-network methods. The 2016 book is freely available online; it predates the transformer era.
• Deep Learning with Python, third edition, François Chollet and Matthew Watson: practical deep learning with Python and Keras. The publisher's page includes the contents and sample material.
• Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper: working with text, corpora, and language analysis using NLTK. The online version is updated for Python 3.
• Foundations of Statistical Natural Language Processing, Christopher Manning and Hinrich Schütze: a 1999 reference on statistical approaches to language. Its companion site provides supporting material and sample chapters.

Research and explanations🔗

• arXiv: machine learning and statistical machine learning: recent preprints. An arXiv posting is not itself evidence of peer review; use the paper's publication record when that distinction matters.
• Christopher Olah's blog: visual and mathematical explanations of neural networks and representations.
• Andrej Karpathy's blog: essays and technical explanations about neural networks, training, and research practice.
• Berkeley Artificial Intelligence Research blog: research explained by the people doing it, with links to papers and project pages.
• Google's Machine Learning Glossary: a reference for unfamiliar terminology.
• Efficient Processing of Deep Neural Networks: A Tutorial and Survey: a 2017 survey of the computation, memory, and hardware considerations behind efficient neural networks. Useful background rather than a guide to the latest accelerators.
• Data Science Blogs: a directory for discovering additional technical writers and research groups.

Tools and datasets🔗

• Jupyter nbviewer: share a rendered view of a notebook without asking readers to install Jupyter. It displays notebook output rather than running a live kernel.
• Awesome Machine Learning: a directory of libraries and software organised by programming language. Useful for discovery; check a project's own documentation before adopting it.
• NVIDIA Technical Blog: implementation and performance articles on GPU computing and AI.
• Deeplearning4j documentation: neural-network tooling for the Java ecosystem.
• Apple ARKit: documentation for building augmented-reality applications on Apple platforms.
• Kaggle Datasets: datasets across a wide range of subjects, often accompanied by exploratory notebooks.
• Linguistic Data Consortium catalogue: language and speech resources. Availability and licensing vary by collection.
• Nasdaq Data Link, formerly Quandl: financial, economic, and alternative data. Check the access terms for the particular dataset you need.

For any dataset, check its licence, provenance, and train/test split before using it in an experiment.

Communities🔗

• r/learnmachinelearning: questions and discussion about learning the subject.
• r/MachineLearning: research papers, projects, and technical discussion.
• r/ReproducibleML: discussions and links focused on reproducing machine-learning work.
• r/textdatamining: text analysis and natural-language processing.
• r/dataisbeautiful: examples and discussion of data visualisation.

Historical material🔗

These links preserve useful material from earlier versions of this list. They are most useful for tracing how ideas developed; code examples may depend on older software.

• Harvard CS109, 2015 materials: the repository of lectures, assignments, and notebooks from the earlier data-science course.
• Stanford CS231n, 2017 lectures: a recorded introduction to neural networks for vision.
• Stanford CS20SI: the TensorFlow-era course on deep learning for research.
• TensorFlow Neural Machine Translation tutorial: an archived implementation of sequence-to-sequence translation, useful alongside papers from that period.
• Deep Learning Papers Reading Roadmap and Highly Cited Deep Learning Papers: reading lists centred on earlier deep-learning literature, not rankings of current research.
• Denny Britz's paper notes and aleju's paper summaries: examples of concise research notes.
• Machine Learning Cheat Sheet: equations and diagrams for revisiting classical methods.
• Awesome TensorFlow: a collection of TensorFlow projects and learning resources.
• MathBox and Drawing in Jupyter: experiments in mathematical visualisation and notebook-based drawing.