Hands-On Deep Learning with R: A practical guide to designing, building, and improving neural network models using R

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Machine Learning Basics

Welcome to Hands-On Deep Learning with R! This book will take you through all of the steps that are necessary to code deep learning models using the R statistical programming language. It begins with simple examples as the first step for those just getting started, along with a review of the foundational elements of deep learning for those with more experience. As you progress through this book, you will learn how to code increasingly complex deep learning solutions for a wide variety of tasks. However, regardless of the complexity, each chapter will carefully detail each step. This is so that all topics and concepts can be fully comprehended and the reason for every line of code is completely explained.

In this chapter, we will go through a quick overview of the machine learning process as it will form a base for the subsequent chapters of this book.&...

Key benefits

Understand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific problem

Improve models using parameter tuning, feature engineering, and ensembling

Apply advanced neural network models such as deep autoencoders and generative adversarial networks (GANs) across different domains

Description

Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming. This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You’ll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems. By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms.

Who is this book for?

This book is for data scientists, machine learning engineers, and deep learning developers who are familiar with machine learning and are looking to enhance their knowledge of deep learning using practical examples. Anyone interested in increasing the efficiency of their machine learning applications and exploring various options in R will also find this book useful. Basic knowledge of machine learning techniques and working knowledge of the R programming language is expected.

What you will learn

Design a feedforward neural network to see how the activation function computes an output

Create an image recognition model using convolutional neural networks (CNNs)

Prepare data, decide hidden layers and neurons and train your model with the backpropagation algorithm

Apply text cleaning techniques to remove uninformative text using NLP

Build, train, and evaluate a GAN model for face generation

Understand the concept and implementation of reinforcement learning in R

What do you get with Print?