• History and Motivation
• Evolution and DL
• Neural Nets

• SGD and backpropagation
• Backprop in practice
• NN training

• Parameter transformation
• Convolutional Nets
• Natural signals' properties

• 1 Dimentionsal Convolutional Nets

• Optimization
• Autograd
• CNNs (again)

• CNN applications
• Recurrent Nets and attention
• Training Recurrent Nets

• Energy-based models
• Self-supervised learning (SSL), Explainable Boosting Machines (EBM)
• Autoencoders

• Contrastive methods
• Regularised latent
• Training Variable Autoencoders

• Sparsity
• World models, Generative Adversarial Networks (GANs)
• Training GANs

• CV SSL
• Predictive Control

• Activations
• Losses
• PPUU

• Deep Learning for Natural Language Processing (NLP)
• Attention and transformers

• Graph Convolutional Networks (GCNs)

• Structured prediction
• Graphical Methods
• Regularization and Bayesian methods

• Interface for latent-variable EBMs
• Training latent-variable EBMs

This is my personal repository containing my notes and solutions to the assignments of the course. I am not affiliated with NYU or Yann LeCun in any way, but am just a student learning from the content they provide. Any mistakes in the solutions are mine and not the course's, so don't hesitate to correct it if you find any.

• NYU course repository
• NYU course website
• Course lectures