Udacity
Project-reviewed program covering neural networks, CNNs, RNNs, GANs, and deployment with PyTorch, with human feedback on each submitted project. Worth it if you want a portfolio of graded projects and mentor support rather than videos alone.
Programmers ready for the deep end. This is an intermediate course in Deep Learning, running about ~50 hours (about 2-3 months part-time).
$249/mo subscription (4-month bundle $212/mo). Udacity All Access subscription pricing seen 2026-11-01; promotions common. Estimated 50 hours of content.
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.