lexfridman/mit-deep-learning Alternatives

choose for the active MIT 6.S191 lab materials with official assignments and Colab support.

Lab Materials for MIT 6.S191: Introduction to Deep Learning

choose for a modern, comprehensive university deep learning course with PyTorch notebooks and exercises.

Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023

choose for a grab-bag of standalone ML/DL code examples across many topics instead of a single course.

A collection of machine learning examples and tutorials.

choose for a book-driven fastai deep learning course with notebooks if you prefer the fastai pedagogy; fewer recent updates.

The fastai book, published as Jupyter Notebooks

choose for in-depth annotated implementations of recent papers to learn advanced deep learning by reading code.

🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

choose only if you need classic TensorFlow 1.x tutorials with video walkthroughs.

TensorFlow Tutorials with YouTube Videos

choose for a dedicated hands-on deep RL course instead of a broad DL overview.

A course in reinforcement learning in the wild

choose for a collection of standalone Keras GAN implementations to study GAN variants; stale and narrow.

Keras implementations of Generative Adversarial Networks.

choose for practical computer vision model notebooks (YOLO/SAM) when your focus is CV, not general deep learning.

A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.