Alfredo Canziani
37.9K subscribers
1:43:43
14 – From latent-variable EBM (K-means, sparse coding) to target prop to autoencoders, step-by-step
Alfredo Canziani
2.2K views • 1 year ago
53:14
07 – Classification, an energy perspective – PyTorch 5-step training code
Alfredo Canziani
2.5K views • 1 year ago
1:47:39
06 – Classification, an energy perspective – Backprop and contrastive learning
Alfredo Canziani
3.8K views • 1 year ago
50:30
05 – Classification, an energy perspective – Notation and introduction
Alfredo Canziani
4.4K views • 1 year ago
1:07:19
03 – Inference with neural nets
Alfredo Canziani
10K views • 1 year ago
2:09
00 – Intro to NYU Deep Learning Fall 2022 playlist
Alfredo Canziani
12K views • 1 year ago
1:05:28
10P – Non-contrastive joint embedding methods (JEMs) for self-supervised learning (SSL)
Alfredo Canziani
3.8K views • 1 year ago
56:52
09P – Contrastive joint embedding methods (JEMs) for self-supervised learning (SSL)
Alfredo Canziani
8K views • 1 year ago
2:12:36
14L – Lagrangian backpropagation, final project winners, and Q&A session
Alfredo Canziani
5.2K views • 2 years ago
1:51:32
13L – Optimisation for Deep Learning
Alfredo Canziani
6.5K views • 2 years ago
1:54:23
07L – PCA, AE, K-means, Gaussian mixture model, sparse coding, and intuitive VAE
Alfredo Canziani
9.2K views • 2 years ago
1:54:44
08L – Self-supervised learning and variational inference
Alfredo Canziani
8.5K views • 2 years ago
2:00:29
09L – Differentiable associative memories, attention, and transformers
Alfredo Canziani
7.9K views • 2 years ago
1:14:45
14 – Prediction and Planning Under Uncertainty
Alfredo Canziani
4.3K views • 2 years ago
1:48:54
06L – Latent variable EBMs for structured prediction
Alfredo Canziani
8.9K views • 2 years ago
1:51:31
05L – Joint embedding method and latent variable energy based models (LV-EBMs)
Alfredo Canziani
22K views • 2 years ago
1:01:22
13 – The Truck Backer-Upper
Alfredo Canziani
3K views • 2 years ago
51:41
04L – ConvNet in practice
Alfredo Canziani
10K views • 2 years ago
1:59:47
03L – Parameter sharing: recurrent and convolutional nets
Alfredo Canziani
19K views • 2 years ago
1:42:27
02L – Modules and architectures
Alfredo Canziani
21K views • 2 years ago
1:51:04
01L – Gradient descent and the backpropagation algorithm
Alfredo Canziani
52K views • 2 years ago
1:10:23
12 – Planning and control
Alfredo Canziani
5.2K views • 2 years ago
1:57:56
12L – Low resource machine translation
Alfredo Canziani
3.6K views • 2 years ago
57:34
11 – Graph Convolutional Networks (GCNs)
Alfredo Canziani
8.3K views • 2 years ago
1:36:13
10L – Self-supervised learning in computer vision
Alfredo Canziani
30K views • 2 years ago
1:55:04
11L – Speech recognition and Graph Transformer Networks
Alfredo Canziani
10K views • 2 years ago
1:12:01
10 – Self / cross, hard / soft attention and the Transformer
Alfredo Canziani
34K views • 2 years ago
1:07:51
09 – AE, DAE, and VAE with PyTorch; generative adversarial networks (GAN) and code
Alfredo Canziani
16K views • 2 years ago
1:00:35
08 – From LV-EBM to target prop to (vanilla, denoising, contractive, variational) autoencoder
Alfredo Canziani
7.6K views • 2 years ago
56:42
07 – Unsupervised learning: autoencoding the targets
Alfredo Canziani
7.2K views • 2 years ago
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