AI Learning Resources
A curated collection of the best courses, visual guides, papers, and tools for understanding modern AI.
Neural Networks: Zero to Hero↗
Andrej Karpathy
Build neural networks from scratch in code — from backprop to GPT.
fast.ai — Practical Deep Learning↗
Jeremy Howard & Rachel Thomas
Top-down, code-first approach to deep learning with practical results from day one.
CS229: Machine Learning↗
Stanford — Andrew Ng
The foundational Stanford ML course covering supervised, unsupervised, and reinforcement learning.
MIT 6.S191: Intro to Deep Learning↗
MIT
MIT's fast-paced intro covering core deep learning methods and applications.
DeepLearning.AI Specializations↗
Andrew Ng
Structured learning paths from neural network basics through advanced specializations.
UT Austin: Deep Learning (CS394D)↗
Philipp Krähenbühl — UT Austin
A rigorous deep learning course covering fundamentals, computer vision, NLP, RL, and generative modeling — with PyTorch labs.
UT Austin: Advances in Deep Learning (CS395T)↗
Philipp Krähenbühl — UT Austin
Graduate-level course on cutting-edge deep learning research — from modern architectures to frontier topics in vision and language.
The Illustrated Transformer↗
Jay Alammar
The gold-standard visual walkthrough of the transformer architecture.
The Illustrated GPT-2↗
Jay Alammar
Visual guide to how GPT-2 generates text, with clear attention diagrams.
Neural Networks (3Blue1Brown)↗
Grant Sanderson
Beautiful animated explanations of how neural networks learn.
Distill.pub↗
Various researchers
Interactive, peer-reviewed articles on machine learning concepts — visual-first research.
CNN Explainer↗
Georgia Tech
Interactive visualization of how convolutional neural networks process images.
Attention? Attention!↗
Lilian Weng
Comprehensive survey of attention mechanisms in deep learning.
Attention Is All You Need↗
Vaswani et al., 2017
The paper that introduced the transformer architecture.
BERT: Pre-training of Bidirectional Transformers↗
Devlin et al., 2018
Bidirectional pre-training that transformed NLP benchmarks.
Denoising Diffusion Probabilistic Models↗
Ho et al., 2020
The foundational paper behind modern image generation models.
Retrieval-Augmented Generation (RAG)↗
Lewis et al., 2020
Combining retrieval with generation for knowledge-grounded language models.
TensorFlow Playground↗
Tinker with a real neural network in your browser — instantly see how it learns.
Hugging Face↗
Hugging Face
The hub for open-source models, datasets, and ML demos.
Papers With Code↗
Community
ML papers with linked code implementations and benchmark leaderboards.
LLM Visualization↗
Brendan Bycroft
Interactive 3D visualization of how a GPT-style language model works.