Book Image

Deep Learning with R for Beginners

By : Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado
Book Image

Deep Learning with R for Beginners

By: Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado

Overview of this book

Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models. This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R. By the end of this Learning Path, you’ll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.

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Deep Learning with R for Beginners
Mark Hodnett | Joshua F. Wiley | Yuxi (Hayden) Liu | Pablo Maldonado
May, 2019 | 10 hours 12 minutes
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Table of Contents (23 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
About Packt
About Packt
Contributors
Contributors
Preface
Preface
Free Chapter
1
Getting Started with Deep Learning
Getting Started with Deep Learning
What is deep learning?
A conceptual overview of neural networks
Deep neural networks
Some common myths about deep learning
Setting up your R environment
Summary
2
Training a Prediction Model
Training a Prediction Model
Neural networks in R
The problem of overfitting data – the consequences explained
Use case – building and applying a neural network
Summary
3
Deep Learning Fundamentals
Deep Learning Fundamentals
Building neural networks from scratch in R
Using regularization to overcome overfitting
Use case – improving out-of-sample model performance using dropout
Summary
4
Training Deep Prediction Models
Training Deep Prediction Models
Getting started with deep feedforward neural networks
Activation functions
Introduction to the MXNet deep learning library
Use case – using MXNet for classification and regression
Summary
5
Image Classification Using Convolutional Neural Networks
Image Classification Using Convolutional Neural Networks
CNNs
Convolutional layers
Image classification using the MXNet library
References/further reading
Summary
6
Tuning and Optimizing Models
Tuning and Optimizing Models
Evaluation metrics and evaluating performance
Data preparation
Data augmentation
Tuning hyperparameters
Use case—using LIME for interpretability
Summary
7
Natural Language Processing Using Deep Learning
Natural Language Processing Using Deep Learning
Document classification
Advanced deep learning text classification
Summary
8
Deep Learning Models Using TensorFlow in R
Deep Learning Models Using TensorFlow in R
Introduction to the TensorFlow library
TensorFlow models
TensorFlow estimators and TensorFlow runs packages
Summary
9
Anomaly Detection and Recommendation Systems
Anomaly Detection and Recommendation Systems
What is unsupervised learning?
How do auto-encoders work?
Training an auto-encoder in R
Using auto-encoders for anomaly detection
Use case – collaborative filtering
Summary
10
Running Deep Learning Models in the Cloud
Running Deep Learning Models in the Cloud
Setting up a local computer for deep learning
Using AWS for deep learning
Using Azure for deep learning
Using Google Cloud for deep learning
Using Paperspace for deep learning
Summary
11
The Next Level in Deep Learning
The Next Level in Deep Learning
Image classification models
Deploying TensorFlow models
Other deep learning topics
Summary
12
Handwritten Digit Recognition using Convolutional Neural Networks
Handwritten Digit Recognition using Convolutional Neural Networks
What is deep learning and why do we need it?
Handwritten digit recognition using CNNs
Summary
13
Traffic Signs Recognition for Intelligent Vehicles
Traffic Signs Recognition for Intelligent Vehicles
How is deep learning applied in self-driving cars?
Traffic sign recognition using CNN
Dealing with a small training set – data augmentation
Reviewing methods to prevent overfitting in CNNs
Summary
14
Fraud Detection with Autoencoders
Fraud Detection with Autoencoders
Getting ready
Our first examples
Credit card fraud detection with autoencoders
Variational Autoencoders
Text fraud detection
Summary
15
Text Generation using Recurrent Neural Networks
Text Generation using Recurrent Neural Networks
What is so exciting about recurrent neural networks?
RNNs from scratch in R
RNN using Keras
Summary
16
Sentiment Analysis with Word Embedding
Sentiment Analysis with Word Embedding
Warm-up – data exploration
Bag of words benchmark
Word embeddings
Sentiment analysis from movie reviews
Mining sentiment from Twitter
Summary
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think
Index
Index

Contributors

About the authors

Mark Hodnett is a data scientist with over 20 years of industry experience in software development, business intelligence systems, and data science. He has worked in a variety of industries, including CRM systems, retail loyalty, IoT systems, and accountancy. He holds a master's in data science and an MBA. He works in Cork, Ireland, as a senior data scientist with AltViz.

 

 

 

 

 

 

 

Joshua F. Wiley is a lecturer at Monash University, conducting quantitative research on sleep, stress, and health. He earned his Ph.D. from the University of California, Los Angeles and completed postdoctoral training in primary care and prevention. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. He develops or co-develops a number of R packages including Varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.

Yuxi (Hayden) Liu is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his machine learning expertise to computational advertising, recommendation, and network anomaly detection. He published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto. He is an education enthusiast and the author of a series of machine learning books. His first book, the first edition of Python Machine Learning By Example, was a #1 bestseller on Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt.

 

 

 

 

 

 

Pablo Maldonado is an applied mathematician and data scientist with a taste for software development since his days of programming BASIC on a Tandy 1000. As an academic and business consultant, he spends a great deal of his time building applied artificial intelligence solutions for text analytics, sensor and transactional data, and reinforcement learning. Pablo earned his Ph.D. in applied mathematics (with focus on mathematical game theory) at the Universite Pierre et Marie Curie in Paris, France.

 

 

 

 

 

 

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