Deep Learning with PyTorch Quick Start Guide电子书

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Deep Learning with PyTorch Quick Start Guide

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Contributors

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Preface

Who this book is for

What this book covers

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Download the example code files

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Conventions used

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Introduction to PyTorch

What is PyTorch?

Installing PyTorch

Digital Ocean

Tunneling in to IPython

Amazon Web Services (AWS)

Basic PyTorch operations

Default value initialization

Converting between tensors and NumPy arrays

Slicing and indexing and reshaping

In place operations

Loading data

PyTorch dataset loaders

Displaying an image

DataLoader

Creating a custom dataset

Transforms

ImageFolder

Concatenating datasets

Summary

Deep Learning Fundamentals

Approaches to machine learning

Learning tasks

Unsupervised learning

Clustering

Principle component analysis

Reinforcement learning

Supervised learning

Classification

Evaluating classifiers

Features

Handling text and categories

Models

Linear algebra review

Linear models

Gradient descent

Multiple features

The normal equation

Logistic regression

Nonlinear models

Artificial neural networks

The perceptron

Summary

Computational Graphs and Linear Models

autograd

Computational graphs

Linear models

Linear regression in PyTorch

Saving models

Logistic regression

Activation functions in PyTorch

Multi-class classification example

Summary

Convolutional Networks

Hyper-parameters and multilayered networks

Benchmarking models

Convolutional networks

A single convolutional layer

Multiple kernels

Multiple convolutional layers

Pooling layers

Building a single-layer CNN

Building a multiple-layer CNN

Batch normalization

Summary

Other NN Architectures

Introduction to recurrent networks

Recurrent artificial neurons

Implementing a recurrent network

Long short-term memory networks

Implementing an LSTM

Building a language model with a gated recurrent unit

Summary

Getting the Most out of PyTorch

Multiprocessor and distributed environments

Using a GPU

Distributed environments

torch.distributed

torch.multiprocessing

Optimization techniques

Optimizer algorithms

Learning rate scheduler

Parameter groups

Pretrained models

Implementing a pretrained model

Summary

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