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