PyTorch Deep Learning Hands-On电子书

PyTorch Deep Learning Hands-On

PyTorch Deep Learning Hands-On

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Contributors

About the authors

About the reviewers

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

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Reviews

1. Deep Learning Walkthrough and PyTorch Introduction

Understanding PyTorch's history

What is PyTorch?

Installing PyTorch

What makes PyTorch popular?

Using computational graphs

Using static graphs

Using dynamic graphs

Exploring deep learning

Getting to know different architectures

Fully connected networks

Encoders and decoders

Recurrent neural networks

Recursive neural networks

Convolutional neural networks

Generative adversarial networks

Reinforcement learning

Getting started with the code

Learning the basic operations

The internals of PyTorch

Summary

References

2. A Simple Neural Network

Introduction to the neural network

The problem

Dataset

Novice model

Autograd

Autograd attributes of a tensor

Building the graph

Finding error

Backpropagation

Parameter update

The PyTorch way

High-level APIs

nn.Module

apply()

cuda() and cpu()

train() and eval()

parameters()

zero_grad()

Other layers

The functional module

The loss function

Optimizers

Summary

References

3. Deep Learning Workflow

Ideation and planning

Design and experimentation

The dataset and DataLoader classes

Utility packages

torchvision

torchtext

torchaudio

Model implementation

Bottleneck and profiling

Training and validation

Ignite

Engine

Events

Metrics

Saving checkpoints

Summary

References

4. Computer Vision

Introduction to CNNs

Computer vision with PyTorch

Simple CNN

Model

Semantic segmentation

LinkNet

Deconvolution

Skip connections

Model

ConvBlock

DeconvBlock

Pooling

EncoderBlock

DecoderBlock

Summary

References

5. Sequential Data Processing

Introduction to recurrent neural networks

The problem

Approaches

Simple RNN

Word embedding

RNNCell

Utilities

Pad sequence

Pack sequence

Encoder

Classifier

Dropout

Training

Advanced RNNs

LSTM

GRUs

Architecture

LSTMCell and GRUCell

LSTMs and GRUs

Increasing the number of layers

Bidirectional RNN

Classifier

Attention

Recursive neural networks

Reduce

Tracker

SPINN

Summary

References

6. Generative Networks

Defining the approaches

Autoregressive models

PixelCNN

Masked convolution

Gated PixelCNN

WaveNet

GANs

Simple GAN

CycleGAN

Summary

References

7. Reinforcement Learning

The problem

Episodic versus continuous tasks

Cumulative discounted rewards

Markov decision processes

The solution

Policies and value functions

Bellman equation

Finding the optimal Q-function

Deep Q-learning

Experience replay

Gym

Summary

References

8. PyTorch to Production

Serving with Flask

Introduction to Flask

Model serving with Flask

A production-ready server

ONNX

MXNet model server

MXNet model archiver

Load testing

Efficiency with TorchScript

Exploring RedisAI

Summary

References

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