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Deep Learning with PyTorch电子书

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152人正在读 | 0人评论 6.2

作       者:Vishnu Subramanian

出  版  社:Packt Publishing

出版时间:2018-02-23

字       数:29.4万

所属分类: 进口书 > 外文原版书 > 电脑/网络

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Build neural network models in text, vision and advanced analytics using PyTorch About This Book ? Learn PyTorch for implementing cutting-edge deep learning algorithms. ? Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; ? Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Who This Book Is For This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected. What You Will Learn ? Use PyTorch for GPU-accelerated tensor computations ? Build custom datasets and data loaders for images and test the models using torchvision and torchtext ? Build an image classifier by implementing CNN architectures using PyTorch ? Build systems that do text classification and language modeling using RNN, LSTM, and GRU ? Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning ? Learn how to mix multiple models for a powerful ensemble model ? Generate new images using GAN’s and generate artistic images using style transfer In Detail Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease. Style and approach An end-to-end guide that teaches you all about PyTorch and how to implement it in various scenarios.
目录展开

Title Page

Copyright and Credits

Deep Learning with PyTorch

Dedication

Packt Upsell

Why subscribe?

PacktPub.com

Foreword

Contributors

About the author

About the reviewer

Packt is searching for authors like you

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

Get in touch

Reviews

Getting Started with Deep Learning Using PyTorch

Artificial intelligence

The history of AI

Machine learning

Examples of machine learning in real life

Deep learning

Applications of deep learning

Hype associated with deep learning

The history of deep learning

Why now?

Hardware availability

Data and algorithms

Deep learning frameworks

PyTorch

Summary

Building Blocks of Neural Networks

Installing PyTorch

Our first neural network

Data preparation

Scalar (0-D tensors)

Vectors (1-D tensors)

Matrix (2-D tensors)

3-D tensors

Slicing tensors

4-D tensors

5-D tensors

Tensors on GPU

Variables

Creating data for our neural network

Creating learnable parameters

Neural network model

Network implementation

Loss function

Optimize the neural network

Loading data

Dataset class

DataLoader class

Summary

Diving Deep into Neural Networks

Deep dive into the building blocks of neural networks

Layers – fundamental blocks of neural networks

Non-linear activations

Sigmoid

Tanh

ReLU

Leaky ReLU

PyTorch non-linear activations

The PyTorch way of building deep learning algorithms

Model architecture for different machine learning problems

Loss functions

Optimizing network architecture

Image classification using deep learning

Loading data into PyTorch tensors

Loading PyTorch tensors as batches

Building the network architecture

Training the model

Summary

Fundamentals of Machine Learning

Three kinds of machine learning problems

Supervised learning

Unsupervised learning

Reinforcement learning

Machine learning glossary

Evaluating machine learning models

Training, validation, and test split

Simple holdout validation

K-fold validation

K-fold validation with shuffling

Data representativeness

Time sensitivity

Data redundancy

Data preprocessing and feature engineering

Vectorization

Value normalization

Handling missing values

Feature engineering

Overfitting and underfitting

Getting more data

Reducing the size of the network

Applying weight regularization

Dropout

Underfitting

Workflow of a machine learning project

Problem definition and dataset creation

Measure of success

Evaluation protocol

Prepare your data

Baseline model

Large model enough to overfit

Applying regularization

Learning rate picking strategies

Summary

Deep Learning for Computer Vision

Introduction to neural networks

MNIST – getting data

Building a CNN model from scratch

Conv2d

Pooling

Nonlinear activation – ReLU

View

Linear layer

Training the model

Classifying dogs and cats – CNN from scratch

Classifying dogs and cats using transfer learning

Creating and exploring a VGG16 model

Freezing the layers

Fine-tuning VGG16

Training the VGG16 model

Calculating pre-convoluted features

Understanding what a CNN model learns

Visualizing outputs from intermediate layers

Visualizing weights of the CNN layer

Summary

Deep Learning with Sequence Data and Text

Working with text data

Tokenization

Converting text into characters

Converting text into words

N-gram representation

Vectorization

One-hot encoding

Word embedding

Training word embedding by building a sentiment classifier

Downloading IMDB data and performing text tokenization

torchtext.data

torchtext.datasets

Building vocabulary

Generate batches of vectors

Creating a network model with embedding

Training the model

Using pretrained word embeddings

Downloading the embeddings

Loading the embeddings in the model

Freeze the embedding layer weights

Recursive neural networks

Understanding how RNN works with an example

LSTM

Long-term dependency

LSTM networks

Preparing the data

Creating batches

Creating the network

Training the model

Convolutional network on sequence data

Understanding one-dimensional convolution for sequence data

Creating the network

Training the model

Summary

Generative Networks

Neural style transfer

Loading the data

Creating the VGG model

Content loss

Style loss

Extracting the losses

Creating loss function for each layers

Creating the optimizer

Training

Generative adversarial networks

Deep convolutional GAN

Defining the generator network

Transposed convolutions

Batch normalization

Generator

Defining the discriminator network

Defining loss and optimizer

Training the discriminator

Training the discriminator with real images

Training the discriminator with fake images

Training the generator network

Training the complete network

Inspecting the generated images

Language modeling

Preparing the data

Generating the batches

Batches

Backpropagation through time

Defining a model based on LSTM

Defining the train and evaluate functions

Training the model

Summary

Modern Network Architectures

Modern network architectures

ResNet

Creating PyTorch datasets

Creating loaders for training and validation

Creating a ResNet model

Extracting convolutional features

Creating a custom PyTorch dataset class for the pre-convoluted features and loader

Creating a simple linear model

Training and validating the model

Inception

Creating an Inception model

Extracting convolutional features using register_forward_hook

Creating a new dataset for the convoluted features

Creating a fully connected model

Training and validating the model

Densely connected convolutional networks – DenseNet

DenseBlock

DenseLayer

Creating a DenseNet model

Extracting DenseNet features

Creating a dataset and loaders

Creating a fully connected model and train

Model ensembling

Creating models

Extracting the image features

Creating a custom dataset along with data loaders

Creating an ensembling model

Training and validating the model

Encoder-decoder architecture

Encoder

Decoder

Summary

What Next?

What next?

Overview

Interesting ideas to explore

Object detection

Image segmentation

OpenNMT in PyTorch

Alien NLP

fast.ai – making neural nets uncool again

Open Neural Network Exchange

How to keep yourself updated

Summary

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