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Artificial Intelligence

Machine learning, neural networks, LLMs, and the math behind it all. Resources I've actually worked through — not just bookmarked.

12 resources

4 types

Jump to YouTube 4 Courses 3 GitHub 3 Articles 2

YouTube Playlists 4 playlists

3Blue1Brown — Neural Networks

The best visual introduction to how neural networks actually work. Covers backpropagation and gradient descent with stunning animations.

BeginnerVisual

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Andrej Karpathy — Neural Nets: Zero to Hero

Build a GPT from scratch in code. Karpathy walks through every line — probably the most practical ML series on YouTube.

IntermediateCoding

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StatQuest — Machine Learning

Josh Starmer explains every ML algorithm with clarity and a lot of enthusiasm. Great for building intuition before going deep into the math.

BeginnerStats

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Yannic Kilcher — Paper Walkthroughs

Deep dives into landmark ML papers — attention, diffusion models, RL. Best watched once you have some foundation.

AdvancedResearch

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Courses 3 courses

fast.ai — Practical Deep Learning

Top-down approach: start building things first, understand the theory after. Free, and genuinely good at getting you to a working model fast.

FreePractical

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deeplearning.ai — ML Specialisation

Andrew Ng's classic. Thorough, well-paced, and covers supervised, unsupervised, and reinforcement learning. Audit it for free on Coursera.

BeginnerStructured

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Stanford CS231n — Computer Vision

Stanford's convolutional neural networks course. Lecture notes and videos are freely available. Dense but worth it.

IntermediateAcademic

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GitHub Repos 3 repos

karpathy/micrograd

A tiny autograd engine in ~150 lines of Python. Reading this is one of the best ways to understand what backpropagation is actually doing.

BeginnerPython

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huggingface/transformers

The go-to library for working with modern ML models. Docs are thorough and the examples folder alone is worth exploring.

IntermediateLLMs

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labmlai/annotated_deep_learning_paper_implementations

60+ ML papers implemented in PyTorch with detailed annotations. Transformers, diffusion, GANs — all in one place.

AdvancedPyTorch

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Articles & Guides 2 reads

Distill.pub

Interactive, visual essays on ML concepts. Some of the clearest writing on attention mechanisms and feature visualisation I've found.

VisualResearch

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Lilian Weng's Blog

Deep, well-referenced posts on RL, diffusion, and LLMs. Dense reading but consistently one of the best technical blogs in the field.

IntermediateTechnical

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