- 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 modifications of the notebooks 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.