MIT · on MIT OpenCourseWare
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.
Programmers ready for the deep end. This is an intermediate course in Deep Learning, running about 9 lectures plus 3 software labs (~2 weeks intensive).
Free. 2026 edition runs Mar 30 - May 25; lectures and labs released weekly and remain open-source under MIT license.
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.