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Index
Title Page
Copyright and Credits
R Deep Learning Essentials Second Edition
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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
Download the color images
Conventions used
Get in touch
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
Additional deep learning resources
Summary
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