d2l.ai (Zhang, Lipton, Li & Smola) · on d2l.ai
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.
Programmers ready for the deep end. This is an intermediate course in Deep Learning, running about Self-paced (full textbook).
Free online (print edition sold separately). Adopted at 500+ universities; runnable notebooks included.
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.
MIT's fast-moving bootcamp updates each year to cover sequence modeling, vision, generative models, RL, and LLMs, with TensorFlow/PyTorch labs including LLM fine-tuning. Great for getting current on the deep learning landscape in a compact format.