R Deep Learning Projects

图书信息

作者	Yuxi (Hayden) Liu,Pablo Maldonado
出版社	Packt Publishing
ISBN	9781788474559
出版时间	2018-02-22
字数	27.7万
分类	Packt Publishing,进口书,外文原版书,电脑,网络

读书简介

5 real-world projects to help you master deep learning concepts About This Book ? Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more ? Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec ? Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices Who This Book Is For Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book. What You Will Learn ? Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec ? Apply neural networks to perform handwritten digit recognition using MXNet ? Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification ? Implement credit card fraud detection with Autoencoders ? Master reconstructing images using variational autoencoders ? Wade through sentiment analysis from movie reviews ? Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks ? Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reduction In Detail R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting. Style and approach This book's unique, learn-as-you-do approach ensures the reader builds on his understanding of deep learning progressively with each project. This book is designed in such a way that implementing each project will empower you with a unique skillset and enable you to implement the next project more confidently.

目录

Title Page

Copyright and Credits

R Deep Learning Projects

Packt Upsell

Why subscribe?

PacktPub.com

Contributors

About the authors

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

Conventions used

Get in touch

Reviews

Handwritten Digit Recognition Using Convolutional Neural Networks

What is deep learning and why do we need it?

What makes deep learning special?

What are the applications of deep learning?

Handwritten digit recognition using CNNs

Get started with exploring MNIST

First attempt – logistic regression

Going from logistic regression to single-layer neural networks

Adding more hidden layers to the networks

Extracting richer representation with CNNs

Summary

Traffic Sign Recognition for Intelligent Vehicles

How is deep learning applied in self-driving cars?

How does deep learning become a state-of-the-art solution?

Traffic sign recognition using CNN

Getting started with exploring GTSRB

First solution – convolutional neural networks using MXNet

Trying something new – CNNs using Keras with TensorFlow

Reducing overfitting with dropout

Dealing with a small training set – data augmentation

Reviewing methods to prevent overfitting in CNNs

Summary

Fraud Detection with Autoencoders

Getting ready

Installing Keras and TensorFlow for R

Installing H2O

Our first examples

A simple 2D example

Autoencoders and MNIST

Outlier detection in MNIST

Credit card fraud detection with autoencoders

Exploratory data analysis

The autoencoder approach – Keras

Fraud detection with H2O

Exercises

Variational Autoencoders

Image reconstruction using VAEs

Outlier detection in MNIST

Text fraud detection

From unstructured text data to a matrix

From text to matrix representation — the Enron dataset

Autoencoder on the matrix representation

Exercises

Summary

Text Generation Using Recurrent Neural Networks

What is so exciting about recurrent neural networks?

But what is a recurrent neural network, really?

LSTM and GRU networks

LSTM

GRU

RNNs from scratch in R

Classes in R with R6

Perceptron as an R6 class

Logistic regression

Multi-layer perceptron

Implementing a RNN

Implementation as an R6 class

Implementation without R6

RNN without derivatives — the cross-entropy method

RNN using Keras

A simple benchmark implementation

Generating new text from old

Exercises

Summary

Sentiment Analysis with Word Embeddings

Warm-up – data exploration

Working with tidy text

The more, the merrier – calculating n-grams instead of single words

Bag of words benchmark

Preparing the data

Implementing a benchmark – logistic regression

Exercises

Word embeddings

word2vec

GloVe

Sentiment analysis from movie reviews

Data preprocessing

From words to vectors

Sentiment extraction

The importance of data cleansing

Vector embeddings and neural networks

Bi-directional LSTM networks

Other LSTM architectures

Exercises

Mining sentiment from Twitter

Connecting to the Twitter API

Building our model

Exploratory data analysis

Using a trained model

Summary

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