Interactive deep learning book with code, math, and discussions. Implemented with PyTorch, NumPy/MXNet, JAX, and TensorFlow. Adopted at 500 universities from 70 countries.

A practical guide to implementing machine learning techniques using popular Python libraries.

A book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models.

This modern and comprehensive Data Science and Machine Learning course on Udemy teaches you the latest skills using Python, TensorFlow 2.0, and other libraries. It covers everything from basic Python programming to advanced topics like Neural Networks, Deep Learning, and Transfer Learning. You'll build real-world projects, gain hands-on experience, and learn to work with popular tools such as Pandas, Scikit-learn, and Matplotlib. By the end, you'll have a portfolio of projects and the skills needed to become a professional Data Scientist, ready for the job market.

A walkthrough on building the GPT tokenizer. This focuses on tokenization in large language models, discussing its complexities, necessary understanding, and its impact on model performance. Taught by Andrej Kaparthy, a founding member of OpenAI and a ex-senior director of AI at Tesla.

Andrej presents a comprehensive lecture on training deep neural networks, explaining the creation and training of a neural network using his library, micrograd, and comparing it to PyTorch. Understanding and training neural networks requires mastering concepts like backpropagation, loss functions, and gradient descent. Taught by Andrej Kaparthy, a founding member of OpenAI and a ex-senior director of AI at Tesla.