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Advanced Deep Learning with R
Advanced Deep Learning with R

Advanced Deep Learning with R: Become an expert at designing, building, and improving advanced neural network models using R

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Advanced Deep Learning with R

Revisiting Deep Learning Architecture and Techniques

Deep learning is part of a broader machine learning and artificial intelligence field that uses artificial neural networks. One of the main advantages of deep learning methods is that they help to capture complex relationships and patterns contained in data. When the relationships and patterns are not very complex, traditional machine learning methods may work well. However, with the availability of technologies that help to generate and process more and more unstructured data, such as images, text, and videos, deep learning methods have become increasingly popular as they are almost a default choice to deal with such data. Computer vision and natural language processing (NLP) are two areas that are seeing interesting applications in a wide variety of fields, such as driverless cars, language translation, computer games, and...

Deep learning with R

We will start by looking at the popularity of deep learning networks and also take a look at a version of some of the important R packages used in this book.

Deep learning trend

Deep learning techniques make use of neural network-based models and have seen increasing interest in the last few years.A Google trends website for the search term deep learning provides the following plot:

The preceding plot has 100 as the peak popularity of a search term, and other numbers are relative to this highest point. It can be observed that the interest in the term deep learning has gradually increased in popularity since around 2014. For the last two years, it has enjoyed peak popularity. One of the reasons for the...

Process of developing a deep network model

Developing a deep learning network model can be broken down into five key steps shown in the following flowchart:

Each step mentioned in the preceding flowchart can have varying requirements based on the type of data used, the type of deep learning network being developed, and also the main objective of developing a model. We will go over each step to develop a general idea about what is involved.

Preparing the data for a deep network model

Developing deep learning neural network models requires the variables to have a certain format. Independent variables may come with a varying scale, with some variable values in decimals and some other variables in thousands. Using such varying...

Deep learning techniques with R and RStudio

The term deep in deep learning refers to a neural network model having several layers, and the learning takes place with the help of data. And based on the type of data used, deep learning may be categorized into two major categories, as shown in the following screenshot:

As shown in the preceding diagram, the type of data used for developing a deep neural network model can be of a structured or unstructured type. In Chapter 2, Deep Neural Networks for Multi-Class Classification, we illustrate the use of a deep learning network for classification problems using structured data where the response variable is of the categorical type. In Chapter 3, Deep Neural Networks for Regression, we illustrate the use of a deep learning network for regression problems using structured data where the response is a continuous type of variable. Chapters...

Summary

Deep learning methods that make use of artificial neural networks have been increasing in popularity in recent years. A number of areas of application involving deep learning methods include driverless cars, image classification, natural language processing, and new image generation. We started this first chapter by looking at the popularity of the deep learning term as reported from a Google trend website. We described a general five-step process for applying deep learning methods and developed some broad ideas about details within each step. We then briefly looked at deep learning techniques covered in each chapter and situations in which they are applied, along with some best practices.

In the next chapter, we get started with an application example and illustrate steps for developing a deep network model for multi-class classification problems.

...
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Key benefits

  • Implement deep learning algorithms to build AI models with the help of tips and tricks
  • Understand how deep learning models operate using expert techniques
  • Apply reinforcement learning, computer vision, GANs, and NLP using a range of datasets

Description

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.

Who is this book for?

This book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.

What you will learn

  • Learn how to create binary and multi-class deep neural network models
  • Implement GANs for generating new images
  • Create autoencoder neural networks for image dimension reduction, image de-noising and image correction
  • Implement deep neural networks for performing efficient text classification
  • Learn to define a recurrent convolutional network model for classification in Keras
  • Explore best practices and tips for performance optimization of various deep learning models

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Publication date : Dec 17, 2019
Length: 352 pages
Edition : 1st
Language : English
ISBN-13 : 9781789534986
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Product Details

Publication date : Dec 17, 2019
Length: 352 pages
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Language : English
ISBN-13 : 9781789534986
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Concepts :
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Table of Contents

19 Chapters
Section 1: Revisiting Deep Learning Basics Chevron down icon Chevron up icon
Revisiting Deep Learning Architecture and Techniques Chevron down icon Chevron up icon
Section 2: Deep Learning for Prediction and Classification Chevron down icon Chevron up icon
Deep Neural Networks for Multi-Class Classification Chevron down icon Chevron up icon
Deep Neural Networks for Regression Chevron down icon Chevron up icon
Section 3: Deep Learning for Computer Vision Chevron down icon Chevron up icon
Image Classification and Recognition Chevron down icon Chevron up icon
Image Classification Using Convolutional Neural Networks Chevron down icon Chevron up icon
Applying Autoencoder Neural Networks Using Keras Chevron down icon Chevron up icon
Image Classification for Small Data Using Transfer Learning Chevron down icon Chevron up icon
Creating New Images Using Generative Adversarial Networks Chevron down icon Chevron up icon
Section 4: Deep Learning for Natural Language Processing Chevron down icon Chevron up icon
Deep Networks for Text Classification Chevron down icon Chevron up icon
Text Classification Using Recurrent Neural Networks Chevron down icon Chevron up icon
Text classification Using Long Short-Term Memory Network Chevron down icon Chevron up icon
Text Classification Using Convolutional Recurrent Neural Networks Chevron down icon Chevron up icon
Section 5: The Road Ahead Chevron down icon Chevron up icon
Tips, Tricks, and the Road Ahead Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(3 Ratings)
5 star 66.7%
4 star 0%
3 star 33.3%
2 star 0%
1 star 0%
Vikram Sreedhar Jan 04, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Recommended for all who want to learn NN CNN, GAN,ANN and deep learning in R. Extremely lucid and articulate in explanation
Amazon Verified review Amazon
Badshah Mukherjee Apr 06, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book provides use cases with important concepts which makes it easier for user to understand DL applications. It makes deep learning interesting to start with instead of just focussing on mathematical jargons.Also once the reader gets to know the applications he can refer other books for deeper understanding into the mathematics of DL. This is the perfect book to start DL.
Amazon Verified review Amazon
Silvia Jul 25, 2020
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Da accompagnare con un altro libro
Amazon Verified review Amazon
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