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The Little Book of Deep Learning

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This book is a short introduction to deep learning for readers with a STEM background. It aims at providing the necessary background to understand landmark AI models for image generation and language understanding.

Author(s): François Fleuret
Edition: 1.1
Publisher: Universite de Geneve
Year: 2023

Language: English
Pages: 159

Contents
List of figures
Foreword
Foundations
Machine Learning
Learning from data
Basis function regression
Under and overfitting
Categories of models
Efficient computation
GPUs, TPUs, and batches
Tensors
Training
Losses
Autoregressive models
Gradient descent
Backpropagation
The value of depth
Training protocols
The benefits of scale
Deep models
Model components
The notion of layer
Linear layers
Activation functions
Pooling
Dropout
Normalizing layers
Skip connections
Attention layers
Token embedding
Positional encoding
Architectures
Multi-Layer Perceptrons
Convolutional networks
Attention models
Applications
Prediction
Image denoising
Image classification
Object detection
Semantic segmentation
Speech recognition
Text-image representations
Reinforcement learning
Synthesis
Text generation
Image generation
The missing bits
Bibliography
Index