【双语字幕】纽约大学《深度学习(PyTorch)》课程(2020) by yann Lecun, Alfredo Canziani
《DEEP LEARNING》by yann Lecun, Alfredo Canziani https://atcold.github.io/pytorch-Deep-Learning/
more info: https://medium.com/@NYUDataScience/yann-lecuns-deep-learning-course-at-cds-is-now-fully-online-accessible-to-all-787ddc8bf0af
视频选集
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Week 1 – Lecture - History, motivation, and evolution of Deep Learning
01:38:57
Week 1 – Practicum - Classification, linear algebra, and visualisation
52:31
Week 2 – Lecture - Stochastic gradient descent and backpropagation
01:43:50
Week 2 – Practicum - Training a neural network
57:03
Week 3 – Lecture - Convolutional neural networks
01:38:16
Week 3 – Practicum - Natural signals properties and CNNs
48:22
Week 4 – Practicum- Listening to convolutions
51:02
Week 5 – Lecture- Optimisation
01:29:06
Week 5 – Practicum - 1D multi-channel convolution and autograd
44:59
Week 6 – Lecture- CNN applications, RNN, and attention
01:28:48
Week 6 – Practicum- RNN and LSTM architectures
53:35
Week 7 – Practicum- Under- and over-complete autoencoders
55:04
Week 7 – Lecture- Energy based models and self-supervised learning
01:37:19
Week 8 – Lecture- Contrastive methods and regularised latent variable models
01:39:27
Week 8 – Practicum- Variational autoencoders
58:05
Week 9 – Lecture- Group sparsity, world model, and generative adversarial networ
01:58:25
Week 9 – Practicum- (Energy-based) Generative adversarial networks
01:15:12
Week 10 – Lecture- Self-supervised learning (SSL) in computer vision (CV)
02:00:42
Week 10 – Practicum- The Truck Backer-Upper
01:00:26
Week 11 – Lecture- PyTorch activation and loss functions
01:53:45
Week 11 – Practicum- Prediction and Policy learning Under Uncertainty (PPUU)
01:23:19
Week 12 – Lecture- Deep Learning for Natural Language Processing (NLP)
01:40:57
Week 12 – Practicum- Attention and the Transformer
01:18:02
Week 13 – Lecture- Graph Convolutional Networks (GCNs)
02:00:23
Week 13 – Practicum- Graph Convolutional Neural Networks (GCN)
01:10:02
Week 14 – Lecture- Structured prediction with energy based models
02:07:31
Week 14 – Practicum- Overfitting and regularization, and Bayesian neural nets
01:11:29
Matrix multiplication, signals, and convolutions
47:02
Week 15 – Practicum part A- Inference for latent variable energy based models (E
59:05
Week 15 – Practicum part B- Training latent variable energy based models (EBMs)
39:21
Supervised and self-supervised transfer learning (with PyTorch Lightning)
01:11:24
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