NYU Deep Learning

NYU Deep Learning

Alfredo Canziani via YouTube Direct link

01L – Gradient descent and the backpropagation algorithm

2 of 31

2 of 31

01L – Gradient descent and the backpropagation algorithm

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NYU Deep Learning

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  1. 1 01 – History and resources
  2. 2 01L – Gradient descent and the backpropagation algorithm
  3. 3 02 – Neural nets: rotation and squashing
  4. 4 02L – Modules and architectures
  5. 5 03 – Tools, classification with neural nets, PyTorch implementation
  6. 6 03L – Parameter sharing: recurrent and convolutional nets
  7. 7 04L – ConvNet in practice
  8. 8 04.1 – Natural signals properties and the convolution
  9. 9 04.2 – Recurrent neural networks, vanilla and gated (LSTM)
  10. 10 05L – Joint embedding method and latent variable energy based models (LV-EBMs)
  11. 11 05.1 – Latent Variable Energy Based Models (LV-EBMs), inference
  12. 12 05.2 – But what are these EBMs used for?
  13. 13 06L – Latent variable EBMs for structured prediction
  14. 14 06 – Latent Variable Energy Based Models (LV-EBMs), training
  15. 15 07L – PCA, AE, K-means, Gaussian mixture model, sparse coding, and intuitive VAE
  16. 16 07 – Unsupervised learning: autoencoding the targets
  17. 17 08L – Self-supervised learning and variational inference
  18. 18 08 – From LV-EBM to target prop to (vanilla, denoising, contractive, variational) autoencoder
  19. 19 09L – Differentiable associative memories, attention, and transformers
  20. 20 09 – AE, DAE, and VAE with PyTorch; generative adversarial networks (GAN) and code
  21. 21 10L – Self-supervised learning in computer vision
  22. 22 10 – Self / cross, hard / soft attention and the Transformer
  23. 23 11L – Speech recognition and Graph Transformer Networks
  24. 24 11 – Graph Convolutional Networks (GCNs)
  25. 25 12L – Low resource machine translation
  26. 26 12 – Planning and control
  27. 27 13L – Optimisation for Deep Learning
  28. 28 13 – The Truck Backer-Upper
  29. 29 14L – Lagrangian backpropagation, final project winners, and Q&A session
  30. 30 14 – Prediction and Planning Under Uncertainty
  31. 31 AI2S Xmas Seminar - Dr. Alfredo Canziani (NYU) - Energy-Based Self-Supervised Learning

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