This is the YouTube channel of the Deep Learning course 2021 at the University of Amsterdam.
Course website: http://uvadlc.github.io
Notebook website: https://uvadlc-notebooks.readthedocs.io/en/latest/
Notebook GitHub: https://github.com/phlippe/uvadlc_notebooks

Tutorial 16: Meta-Learning - Learning to Learn (Part 3)
19:58
Tutorial 16: Meta-Learning - Learning to Learn (Part 3)
Tutorial 16: Meta-Learning - Learning to Learn (Part 2)
10:25
Tutorial 16: Meta-Learning - Learning to Learn (Part 2)
Tutorial 16: Meta-Learning - Learning to Learn (Part 1)
13:39
Tutorial 16: Meta-Learning - Learning to Learn (Part 1)
Tutorial 17: Self Supervised Contrastive Learning with SimCLR (Part 2)
9:04
Tutorial 17: Self Supervised Contrastive Learning with SimCLR (Part 2)
Tutorial 17: Self Supervised Contrastive Learning with SimCLR (Part 1)
21:15
Tutorial 17: Self Supervised Contrastive Learning with SimCLR (Part 1)
Tutorial 15: Vision Transformers
17:40
Tutorial 15: Vision Transformers
Introduction to the Tutorials
5:29
Introduction to the Tutorials
Tutorial 2: Introduction to PyTorch (Part 1)
19:03
Tutorial 2: Introduction to PyTorch (Part 1)
Tutorial 2: Introduction to PyTorch (Part 2)
24:02
Tutorial 2: Introduction to PyTorch (Part 2)
Tutorial 3: Activation functions (Part 1)
12:37
Tutorial 3: Activation functions (Part 1)
Tutorial 3: Activation functions (Part 2)
25:29
Tutorial 3: Activation functions (Part 2)
Tutorial 5: Inception, ResNet and DenseNet (Part 1)
16:04
Tutorial 5: Inception, ResNet and DenseNet (Part 1)
Tutorial 5: Inception, ResNet and DenseNet (Part 2)
18:38
Tutorial 5: Inception, ResNet and DenseNet (Part 2)
Tutorial 5: Inception, ResNet and DenseNet (Part 3)
9:31
Tutorial 5: Inception, ResNet and DenseNet (Part 3)
Tutorial 4: Optimization and Initialization (Part 1)
27:26
Tutorial 4: Optimization and Initialization (Part 1)
Tutorial 4: Optimization and Initialization (Part 2)
17:52
Tutorial 4: Optimization and Initialization (Part 2)
Tutorial 7: Graph Neural Networks (Part 2)
17:17
Tutorial 7: Graph Neural Networks (Part 2)
Tutorial 7: Graph Neural Networks (Part 1)
19:12
Tutorial 7: Graph Neural Networks (Part 1)
Tutorial 6: Transformers and MH Attention (Part 1)
16:59
Tutorial 6: Transformers and MH Attention (Part 1)
Tutorial 6: Transformers and MH Attention (Part 2)
12:05
Tutorial 6: Transformers and MH Attention (Part 2)
Tutorial 6: Transformers and MH Attention (Part 3)
13:40
Tutorial 6: Transformers and MH Attention (Part 3)
Tutorial 8: Deep Energy-Based Generative Models (Part 1)
14:51
Tutorial 8: Deep Energy-Based Generative Models (Part 1)
Tutorial 8: Deep Energy-Based Generative Models (Part 2)
19:59
Tutorial 8: Deep Energy-Based Generative Models (Part 2)
Tutorial 9: Deep Autoencoders (Part 1)
11:06
Tutorial 9: Deep Autoencoders (Part 1)
Tutorial 9: Deep Autoencoders (Part 2)
12:28
Tutorial 9: Deep Autoencoders (Part 2)
Tutorial 10: Adversarial Attacks (Part 2)
10:51
Tutorial 10: Adversarial Attacks (Part 2)
Tutorial 10: Adversarial Attacks (Part 1)
11:01
Tutorial 10: Adversarial Attacks (Part 1)
Tutorial 10: Adversarial Attacks (Part 3)
8:34
Tutorial 10: Adversarial Attacks (Part 3)
Tutorial 11: Normalizing Flows (Part 1)
10:29
Tutorial 11: Normalizing Flows (Part 1)
Tutorial 11: Normalizing Flows (Part 4)
11:20
Tutorial 11: Normalizing Flows (Part 4)