Deep Learning With R for Beginners by Hodnett, Mark -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Deep Learning with R for Beginners

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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 of this book Conventions used

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Reviews

Getting Started with Deep Learning

What is deep learning? A conceptual overview of neural networks

Neural networks as an extension of linear regression Neural networks as a network of memory cells

Deep neural networks Some common myths about deep learning Setting up your R environment

Deep learning frameworks for R

MXNet Keras

Do I need a GPU (and what is it, anyway)? Setting up reproducible results

Summary

Training a Prediction Model

Neural networks in R

Building neural network models Generating predictions from a neural network

The problem of overfitting data – the consequences explained Use case – building and applying a neural network Summary

Deep Learning Fundamentals

Building neural networks from scratch in R

Neural network web application Neural network code Back to deep learning

The symbol, X, y, and ctx parameters The num.round and begin.round parameters The optimizer parameter The initializer parameter The eval.metric and eval.data parameters The epoch.end.callback parameter The array.batch.size parameter

Using regularization to overcome overfitting

L1 penalty

L1 penalty in action

L2 penalty

L2 penalty in action Weight decay (L2 penalty in neural networks)

Ensembles and model-averaging

Use case – improving out-of-sample model performance using dropout Summary

Training Deep Prediction Models

Getting started with deep feedforward neural networks Activation functions Introduction to the MXNet deep learning library

Deep learning layers Building a deep learning model

Use case – using MXNet for classification and regression

Data download and exploration Preparing the data for our models The binary classification model The regression model Improving the binary classification model The unreasonable effectiveness of data

Summary

Image Classification Using Convolutional Neural Networks

CNNs Convolutional layers

Pooling layers Dropout Flatten layers, dense layers, and softmax

Image classification using the MXNet library

Base model (no convolutional layers) LeNet Classification using the fashion MNIST dataset

References/further reading Summary

Tuning and Optimizing Models

Evaluation metrics and evaluating performance

Types of evaluation metric Evaluating performance

Data preparation

Different data distributions Data partition between training, test, and validation sets Standardization Data leakage

Data augmentation

Using data augmentation to increase the training data

Test time augmentation

Using data augmentation in deep learning libraries

Tuning hyperparameters

Grid search Random search

Use case—using LIME for interpretability

Model interpretability with LIME

Summary

Natural Language Processing Using Deep Learning

Document classification

The Reuters dataset Traditional text classification Deep learning text classification Word vectors Comparing traditional text classification and deep learning

Advanced deep learning text classification

1D convolutional neural network model Recurrent neural network model Long short term memory model Gated Recurrent Units model Bidirectional LSTM model Stacked bidirectional model Bidirectional with 1D convolutional neural network model Comparing the deep learning NLP architectures

Summary

Deep Learning Models Using TensorFlow in R

Introduction to the TensorFlow library

Using TensorBoard to visualize deep learning networks

TensorFlow models

Linear regression using TensorFlow Convolutional neural networks using TensorFlow

TensorFlow estimators and TensorFlow runs packages

TensorFlow estimators TensorFlow runs package

Summary

Anomaly Detection and Recommendation Systems

What is unsupervised learning? How do auto-encoders work?

Regularized auto-encoders

Penalized auto-encoders Denoising auto-encoders

Training an auto-encoder in R

Accessing the features of the auto-encoder model

Using auto-encoders for anomaly detection Use case – collaborative filtering

Preparing the data Building a collaborative filtering model Building a deep learning collaborative filtering model Applying the deep learning model to a business problem

Summary

Running Deep Learning Models in the Cloud

Setting up a local computer for deep learning

How do I know if my model is training on a GPU?

Using AWS for deep learning

A brief introduction to AWS Creating a deep learning GPU instance in AWS Creating a deep learning AMI in AWS

Using Azure for deep learning Using Google Cloud for deep learning Using Paperspace for deep learning Summary

The Next Level in Deep Learning

Image classification models

Building a complete image classification solution

Creating the image data Building the deep learning model Using the saved deep learning model

The ImageNet dataset Loading an existing model Transfer learning

Deploying TensorFlow models Other deep learning topics

Generative adversarial networks Reinforcement learning

Summary

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 Signs 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 Embedding

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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