NYU Center for Data Science · on GitHub
Yann LeCun's lectures paired with Alfredo Canziani's PyTorch practica cover energy-based models, self-supervised learning, graph networks, and more, with notebooks and multilingual notes. For learners who want a research-oriented perspective straight from a founder of the field.
Programmers ready for the deep end. This is an advanced course in Deep Learning, running about 15 weeks of lectures and practica.
Free. No payment required at any point.
Five courses covering neural network foundations, hyperparameter tuning, ML strategy, CNNs, and sequence models and transformers, taught by Andrew Ng. The default next step after an intro ML course for anyone heading toward deep learning roles.
Laurence Moroney's four-course track teaches building CNNs, NLP models, and time-series forecasters in TensorFlow and Keras. Pick it if your target job stack is TensorFlow or you want applied practice after a theory-heavy course.
An interactive, code-first textbook with every concept implemented in PyTorch, NumPy/MXNet, JAX, and TensorFlow, from linear regression through transformers and optimization. Best for learners who want a rigorous reference that doubles as a hands-on course.