R Deep Learning Projects电子书

Title Page

Copyright and Credits

R Deep Learning Projects

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Contributors

About the authors

About the reviewer

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Preface

Who this book is for

What this book covers

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Download the example code files

Conventions used

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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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