Welcome
- License
1 Introduction
- 1.1 Introduction
I Using Torch
Introduction
2 A simple neural network in R
- 2.1 What’s in a network?
  - 2.1.1 Gradient descent
  - 2.1.2 From linear regression to a simple network
- 2.2 A simple network
3 Modifying the simple network to use torch tensors
4 Using autograd
- 4.1 Automatic differentiation with autograd
- 4.2 The simple network, now using autograd
5 Using torch modules
- 5.1 Modules
  - 5.1.1 Base modules (“layers”)
  - 5.1.2 Container modules (“models”)
- 5.2 Simple network using modules
6 Using torch optimizers
II Image Recognition
Introduction
7 Classifying images
8 Brain Image Segmentation with U-Net
III Time series
Introduction
9 Torch transformer modules
10 Sequence-to-sequence models with attention
11 Torch transformer modules
IV Natural language processing
Introduction
12 Sequence-to-sequence models with attention
13 Torch transformer modules
V Tabular data
Introduction
14 Handling categorical data
VI Generative deep learning
Introduction
15 Generative adversarial networks
16 Variational autoencoders
VII Deep learning on graphs
Introduction
VIII Probabilistic deep learning
Introduction
IX Private and secure deep learning
Introduction
X Research topics
Introduction
References

Applied deep learning with torch from R

1 Introduction

1.1 Introduction

address people new to DL as well as Keras/TF users
DL concepts will be introduced just briefly - name good resources
presuppose general ML knowledge
awareness/ethics