Deep Learning Course (NYU, Spring 2020)
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What you'll learn
This course includes
- 42.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 32 lessons • 42.5 hours of video
Deep Learning Course (NYU, Spring 2020)
32 lessons
• 42.5 hours
Deep Learning Course (NYU, Spring 2020)
32 lessons
• 42.5 hours
- 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 01:43:17
- Week 2 – Practicum: Training a neural network 57:00
- 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 53:34
- 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 01:39:26
- Week 8 – Practicum: Variational autoencoders 58:05
- Week 9 – Lecture: Group sparsity, world model, and generative adversarial networks (GANs) 01:58:25
- Week 9 – Practicum: (Energy-based) Generative adversarial networks 01:15:12
- Week 10 – Practicum: The Truck Backer-Upper 01:00:26
- Week 10 – Lecture: Self-supervised learning (SSL) in computer vision (CV) 02:00:42
- Week 11 – Practicum: Prediction and Policy learning Under Uncertainty (PPUU) 01:23:19
- Week 11 – Lecture: PyTorch activation and loss functions 01:53:45
- Week 12 – Practicum: Attention and the Transformer 01:18:02
- Week 12 – Lecture: Deep Learning for Natural Language Processing (NLP) 01:40:57
- Week 13 – Practicum: Graph Convolutional Neural Networks (GCN) 01:10:02
- Week 13 – Lecture: Graph Convolutional Networks (GCNs) 02:00:23
- Week 14 – Lecture: Structured prediction with energy based models 02:07:31
- Week 14 – Practicum: Overfitting and regularization, and Bayesian neural nets 01:11:28
- Week 15 – Practicum part A: Inference for latent variable energy based models (EBMs) 59:05
- Week 15 – Practicum part B: Training latent variable energy based models (EBMs) 58:57
- Matrix multiplication, signals, and convolutions 47:02
- Supervised and self-supervised transfer learning (with PyTorch Lightning) 01:11:24
- Four decades in Machine Learning: a Personal Journey by Yann LeCun 01:31:23
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