NYU

NYU, through its Center for Data Science, openly publishes its graduate-level Deep Learning course (DS-GA 1008) co-taught by Turing Award winner Yann LeCun and Alfredo Canziani, with every lecture video, slide deck, written note, and PyTorch Jupyter notebook released free on GitHub and YouTube. It is one of the few elite-university deep learning courses that is genuinely advanced and theory-forward, distinguished by LeCun's energy-based-model framing and heavy emphasis on self-supervised and representation learning rather than just applied recipes. The free release was a community effort: materials were translated into 11 languages by over 470 volunteers across 17 time zones. It is best understood as a rigorous, current-research-flavored deep learning curriculum from a top-tier source, not a beginner-friendly bootcamp.

Best for: Learners who already know machine learning fundamentals, linear algebra, and backpropagation and want a rigorous, research-grade view of modern deep learning, especially self-supervised learning, energy-based models, transformers, and graph neural networks, straight from leading practitioners. Ideal for graduate students, ML engineers, and researchers comfortable with a dense, theory-first style and self-paced study.

Look elsewhere if: Complete beginners or those wanting a gentle, hand-held, job-ready certificate path. The pace is described as 'a marathon, not a sprint' (roughly 50 hours of dense content), LeCun's abstract energy-based framing can be confusing, and the free materials carry no certificate, grading, mentorship, or formal credential. People who prefer Andrew Ng-style accessibility should start elsewhere.

Pricing: Free and open access. All course materials (videos, slides, notes, PyTorch notebooks) are published at no cost on GitHub and YouTube; there is no subscription, per-course fee, or audit gate for the self-study version. Taking DS-GA 1008 for actual academic credit requires paid enrollment as an NYU graduate student.

Certificates: The free, openly published version grants no certificate or formal credential. Its value is the knowledge itself and the credibility of learning directly from Yann LeCun and NYU's Center for Data Science, which carries weight for self-directed learners and can be cited in a portfolio, but it is not a verifiable employer-facing certificate. Genuine NYU academic credit for DS-GA 1008 only comes from enrolling in the degree-granting program.