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Deep Learning Course instructed by Yann LeCun and Alfredo Canziani

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NYU Deep Learning Course by Yann LeCun

Course Structure

  • 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

Disclaimer

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.

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Deep Learning Course instructed by Yann LeCun and Alfredo Canziani

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