The fastest way to understand deep nets is often to implement them yourself; this course forces that path. Through a sequence of recorded lectures and accompanying notebooks you build small frameworks and models from scratch (micrograd, MLPs, WaveNet-style convs, then a GPT), gaining intuition about gradients, optimization, tokenization, and practical debugging that reading papers alone rarely gives.
What Sets It Apart
- Lecture-driven, code-first pedagogy: each conceptual lesson is immediately paired with a Jupyter notebook used in the video, so you implement, visualize, and debug the exact code shown. This means the abstractions are grounded in runnable examples rather than only diagrams.
- Progressive curriculum from scalars to transformers: starts with a tiny autodiff engine (micrograd) to teach backprop, then builds character-level language models and incrementally arrives at a GPT — so learners see how small pieces compose into modern LLMs.
- Practical focus on failure modes and diagnostics: lectures emphasize activation/gradient statistics, batchnorm, manual backprop through layers, and hyperparameter behavior — so you learn how to reason about training instability and debugging in real code.
- Lightweight, reproducible artifacts: notebooks and Colab links accompany exercises, licensed MIT and easy to run for educational experiments (not a production library).
Who It's For & Tradeoffs
Great fit if you want a deep, implementation-first understanding of how neural networks and language models are trained and debugged — especially learners comfortable with Python who prefer tinkering with code over abstract summaries. It’s less suitable as a drop-in toolkit for production or large-scale experiments: the repo is pedagogical (minimal dependencies, educational notebooks) rather than an optimized training stack or a model zoo for immediate deployment. Also, it teaches fundamentals and simple-from-scratch implementations rather than providing the latest SOTA engineering optimizations.
Where It Fits
Use this course to build intuition before moving to higher-level libraries (e.g., PyTorch/Transformers for scale) or when teaching/mentoring newcomers who must see and edit every line of computation. For production-grade training, pair the conceptual lessons here with practical runtime tools and libraries afterwards.
