fast.ai
Jeremy Howard's top-down course has you training state-of-the-art image, text, and tabular models in lesson one, then peels back the layers to the math. Ideal for working programmers who want results first and theory second.
Programmers ready for the deep end. This is an intermediate course in Deep Learning, running about 9 lessons, ~90 minutes each plus notebooks.
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.