Course material

Dog vs cat dataset (used in most ipython notebooks)

Background 78 min
Background pdf key 2 min
Linear algebra and gradients pdf key 19 min
Probability, likelihood, sampling, and expectation pdf key 16 min
Tensors pdf key 5 min
Data as tensors ipynb html 9 min
Broadcasting ipynb html 11 min
Setup and tensorboard ipynb html 15 min
Summary pdf key 2 min
First example 107 min
Linear classification and regression pdf key 7 min
Linear regression pdf key 6 min
Linear classification pdf key 8 min
Linear classification in action ipynb html 13 min
Linear multi-class classification pdf key 11 min
Optimization and gradient descent pdf key 12 min
Gradient descent in action ipynb html 10 min
Computation graphs pdf key 6 min
Building a computation graph ipynb html 10 min
Gradients on computation graphs pdf key 10 min
Back propagation in action ipynb html 12 min
Summary pdf key 2 min
Deep networks 135 min
Beyond linear models pdf key 2 min
Limitations of linear models pdf key 9 min
Non-linearities (ReLU) pdf key 11 min
Output representations pdf key 9 min
Loss functions pdf key 8 min
Building a deep network in pytorch ipynb html 15 min
Optimization of deep networks pdf key 6 min
Stochastic gradient descent pdf key 11 min
Mini-batches pdf key 7 min
Momentum pdf key 7 min
Optimization in pytorch ipynb html 20 min
What is a layer? pdf key 6 min
Activation functions pdf key 15 min
Hyper-parameters pdf key 2 min
Summary, a practical guide to deep network design pdf key 7 min
Convolutional Networks 172 min
Images and structure pdf key 4 min
High dimensional inputs pdf key 4 min
Convolutions pdf key 22 min
Convolutional network in pytorch ipynb html 8 min
Convolutional operators and their structure pdf key 21 min
Average Pooling pdf key 6 min
Max Pooling pdf key 6 min
Convolutional operations in pytorch ipynb html 7 min
Receptive fields pdf key 12 min
Design principles of convolutional networks pdf key 16 min
Building efficient convolutional networks ipynb html 13 min
Deep representations and exploiting the structure of the data pdf key 10 min
Examining the structure of deep networks ipynb html 15 min
Dilation pdf key 9 min
Up convolution pdf key 11 min
Summary pdf key 5 min
Making it work 307 min
Practical deep learning pdf key 7 min
Looking at your data pdf key 6 min
Training, validation, and test sets pdf key 14 min
Distribution of data ipynb html 12 min
Network initialization pdf key 12 min
Random initialization pdf key 9 min
Xavier and Kaiming initialization pdf key 20 min
Initialization in pytorch ipynb html 5 min
Optimization pdf key 2 min
Input normalization pdf key 12 min
Vanishing and exploding gradients pdf key 12 min
Normalization pdf key 2 min
Batch normalization pdf key 9 min
Layer normalization pdf key 3 min
Instance normalization pdf key 3 min
Group normalization and local response normalization pdf key 6 min
Where to add normalization pdf key 8 min
Normalizations in pytorch ipynb html 5 min
Residual connections pdf key 13 min
Residual connection in practice ipynb html 8 min
Optimization algorithms pdf key 10 min
Learning rate pdf key 9 min
Learning rate schedules in pytorch ipynb html 10 min
Open Problem: Pruning and compression pdf key 12 min
Overfitting and how to detect it pdf key 7 min
early stopping pdf key 3 min
data augmentation pdf key 12 min
dropout pdf key 11 min
weight decay pdf key 6 min
ensembles pdf key 8 min
Reducing overfitting repo 20 min
Transfer learning pdf key 9 min
Open Problem: Understanding generalization pdf key 13 min
Summary, a practical guide to deep network optimization pdf key 11 min
Computer Vision 254 min
Computer vision tasks pdf key 15 min
Image classification pdf key 13 min
Case Study: AlexNet pdf key 19 min
Case Study: VGG pdf key 11 min
1x1 Convolutions and factorization pdf key 7 min
Case Study: Network in Network pdf key 3 min
Case Study: Inception Architecture pdf key 8 min
Case Study: Residual Networks pdf key 11 min
Factorization and light-weight networks pdf key 5 min
Case Study: MobileNet pdf key 5 min
Using pre-trained architectures ipynb html 11 min
Object Detection pdf key 11 min
Case Study: RCNN pdf key 13 min
Case Study: Faster RCNN pdf key 14 min
Case Study: RetinaNet pdf key 10 min
Segmentation pdf key 10 min
Case Study: FCN pdf key 11 min
Case Study: Dilated convolutional networks pdf key 10 min
Case Study: Mask RCNN pdf key 9 min
Open Problem: Object representations pdf key 12 min
Temporal models pdf key 6 min
3D convolutions pdf key 5 min
2+1D convolutions pdf key 3 min
Case Study: I3D pdf key 6 min
Open Problem: Effective temporal operations pdf key 10 min
Open Problem: What should we infer or label? pdf key 12 min
Summary pdf key 4 min
Sequence modeling 131 min
Sequence models pdf key 6 min
Recurrent Neural Networks pdf key 16 min
Training recurrent networks pdf key 14 min
LSTMs and GRUs pdf key 15 min
Temporal convolutions pdf key 16 min
Sampling in sequence models pdf key 10 min
Case Study: WaveNet pdf key 12 min
Sequence models in pytorch ipynb html 20 min
Attention and transformers pdf key 16 min
Summary pdf key 6 min
Reinforcement learning 164 min
Acting in an environment pdf key 23 min
Imitation learning pdf key 11 min
Dagger pdf key 8 min
Dagger vs Imitation learning ipynb html 30 min
Non-differentiability pdf key 13 min
REINFORCE pdf key 11 min
Policy gradient pdf key 20 min
Gradient free optimization pdf key 17 min
Gradient free optimization in pytorch ipynb html 13 min
Open Problem: Structure vs data pdf key 13 min
Summary pdf key 5 min
Special topics 156 min
Embedding learning 38 min
Learning with an expanding set of labels pdf key 4 min
Embedding learning pdf key 7 min
Contrastive loss pdf key 8 min
Triplet loss pdf key 5 min
Selecting training examples pdf key 11 min
Summary pdf key 4 min
Generative models 80 min
Image generation pdf key 3 min
Autoencoders pdf key 9 min
Variational autoencoders pdf key 12 min
Transforming noise pdf key 4 min
Generative adversarial networks pdf key 11 min
Pix2Pix pdf key 5 min
CycleGAN pdf key 10 min
Image editing pdf key 5 min
Style transfer pdf key 11 min
Open Problem: Understanding generative models and invariances pdf key 8 min
Summary pdf key 2 min
Adversarial attacks 38 min
Fooling deep networks pdf key 5 min
Finding adversarial examples pdf key 11 min
Defense through data augmentation pdf key 5 min
White vs black box attacks pdf key 7 min
Open Problem: Realistic attacks and defenses pdf key 9 min
Summary pdf key 2 min
Final words 39 min
Open Problem: Bias, fairness, and ethics in deep learning pdf key 29 min
Course summary and further topics pdf key 10 min