Deep Learning
by Yann LeCun & Alfredo Canziani · NYU
Our Verdict
Worth it — with caveatsNYU's DS-GA 1008 Deep Learning, taught by Turing Award winner Yann LeCun with Alfredo Canziani, is one of the most advanced free deep learning courses on the open web, and it is best taken as a second course after you already know the basics. The full Spring 2021 edition (DLSP21) is released for free as close-captioned lecture videos, written overviews, and executable PyTorch Jupyter notebooks, with no enrollment, login, or certificate. Its distinctive value is LeCun's energy-based-model framing and heavy emphasis on self-supervised learning, transformers, and graph neural networks, content you rarely find in beginner courses. The honest catch: multiple sources note LeCun's lectures are theory-heavy and 'not so student friendly as Andrew Ng's,' and for-credit NYU students on Coursicle criticized the lectures as high-level 'seminar talks' with homework that felt disconnected from the material, so beginners should not start here. As a free, self-paced resource it carries no cost and no risk, and the real binding constraint is your math and prior-ML readiness, not money.
World-class, genuinely advanced free content from a deep learning pioneer, but it is a graduate course with real prerequisites (machine learning + linear algebra) and a polarizing, theory-forward lecture style. It is an excellent 'second' deep learning course for people who already know the fundamentals, and the wrong choice for true beginners, who should start with fast.ai or the DeepLearning.AI specialization first.
Best for: Learners who have already completed an introductory ML course and a first deep learning course and want depth on modern research themes, especially energy-based models, self-supervised / joint-embedding methods, transformers, and graph neural networks. Ideal for graduate students, ML engineers, and researchers comfortable with linear algebra, calculus, backpropagation, and reading PyTorch, who want LeCun's specific conceptual framing and ~50 hours of dense, current material with runnable notebooks.
Skip if: Complete beginners or anyone who has never trained a neural network. Reviewers consistently flag that LeCun's lectures present the big picture rather than step-by-step mechanics, and one NYU student called it the worst class they took at NYU because homework and the group project felt 'unlinked with class material.' People who need hand-holding, a guided coding curriculum, graded feedback, or a completion certificate should pick a structured MOOC instead.
About This Course
NYU's graduate-level deep learning course covering energy-based models, self-supervised learning, and graph neural networks.
What You'll Learn
Curriculum
History and motivation, gradient descent and backpropagation, neural network inference, modules and architectures, and training. Includes Homework 1 (backpropagation).
Recurrent and convolutional networks, ConvNets in practice, natural signal properties and convolution, vanilla RNNs and LSTMs. Includes Homework 2 (RNN & CNN).
EBMs Parts I-II, inference and training for latent-variable EBMs, applications, and structured prediction. Includes Homework 3.
Advanced EBM concepts, autoencoders (target propagation, PyTorch implementations), GANs, and contrastive vs. regularized joint-embedding methods.
Attention mechanisms and transformer architectures.
Graph transformer networks and graph convolutional networks (Parts I-II).
Planning and control, the Truck Backer-Upper problem, and prediction/planning under uncertainty.
Optimization techniques for deep learning (Parts I-II).
Self-supervised learning for vision, low-resource machine translation, speech recognition with graph transformers, and Lagrangian backpropagation.
Prerequisites
- A prior machine learning course (the for-credit version lists DS-GA 1001 Intro to Data Science and DS-GA 1003 Machine Learning as prerequisites; the free release states DS-GA 1001 or an equivalent ML course)
- Linear algebra and multivariable calculus
- Working understanding of gradient descent and backpropagation
- Python and the ability to read/run PyTorch code in Jupyter notebooks
Instructor
Yann LeCun & Alfredo Canziani
Instructor · NYU
Pros & Cons
Pros
- Taught by Yann LeCun (Turing Award winner, co-inventor of modern ConvNets) with Alfredo Canziani's well-regarded, approachable practical sessions; you get a research pioneer's actual mental model.
- Covers genuinely modern, research-grade topics that most courses skip: energy-based models, self-supervised / joint-embedding learning, transformers, and graph neural networks.
- Completely free and self-paced with no login or paywall: ~50 hours of close-captioned videos, written overviews, and runnable PyTorch Jupyter notebooks, with materials translated by 470+ volunteers into many languages.
- The DLSP21 edition adds polished motion-graphics visualizations, and the GitHub notebooks let you run real code rather than just watching.
Cons
- LeCun's lectures are theory-first and high-level; reviewers describe them as 'not so student friendly as Andrew Ng's' and, from for-credit students, as general 'seminar talks' that omit implementation details.
- Steep prerequisites and pace: one reviewer needed over three months of full-time study, and beginners will likely be lost without prior ML and solid math.
- No certificate, no graded feedback, and no instructor support for self-learners; for-credit students also reported homework and the group project feeling disconnected from lectures.
- The free release is the 2020/2021 edition, so the most recent post-2021 advances (e.g., latest large-scale generative/LLM developments) are not covered.
Alternatives To Consider
Frequently Asked Questions
Is Deep Learning free?
Yes — Deep Learning is free to access. Free. All DLSP21 materials (videos, written notes, and PyTorch notebooks) are openly available with no enrollment, login, or payment, and there is no certificate. The for-credit NYU version (DS-GA 1008) is separate, tuition-based, and requires admission to NYU.
Who is Deep Learning for?
Learners who have already completed an introductory ML course and a first deep learning course and want depth on modern research themes, especially energy-based models, self-supervised / joint-embedding methods, transformers, and graph neural networks. Ideal for graduate students, ML engineers, and researchers comfortable with linear algebra, calculus, backpropagation, and reading PyTorch, who want LeCun's specific conceptual framing and ~50 hours of dense, current material with runnable notebooks.
What will you learn in Deep Learning?
Neural network fundamentals: gradient descent, the backpropagation algorithm, modules, and training mechanics; Convolutional and recurrent networks (including LSTMs) and the natural-signal properties that motivate convolution; Energy-based models in depth, from foundations to inference and training of latent-variable EBMs and structured prediction; Self-supervised and unsupervised learning, autoencoders, GANs, and contrastive vs. regularized joint-embedding methods.
What are the prerequisites for Deep Learning?
A prior machine learning course (the for-credit version lists DS-GA 1001 Intro to Data Science and DS-GA 1003 Machine Learning as prerequisites; the free release states DS-GA 1001 or an equivalent ML course); Linear algebra and multivariable calculus; Working understanding of gradient descent and backpropagation; Python and the ability to read/run PyTorch code in Jupyter notebooks.
Is Deep Learning worth it?
World-class, genuinely advanced free content from a deep learning pioneer, but it is a graduate course with real prerequisites (machine learning + linear algebra) and a polarizing, theory-forward lecture style. It is an excellent 'second' deep learning course for people who already know the fundamentals, and the wrong choice for true beginners, who should start with fast.ai or the DeepLearning.AI specialization first.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on NYU's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- Official course site - NYU Deep Learning DLSP21 (atcold.github.io)
- NYU CDS Medium - 'Yann LeCun's Deep Learning Course is Now Fully Online & Accessible to All'
- Coursicle - DS-GA 1008 student reviews & ratings
- Class Central - NYU Deep Learning SP21 listing
- Dimitris Georgoulas - independent learner review & notes of the NYU 2021 DL course