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Advanced Deep Learning with R

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

Rai

€18.99 per month

4.3 (3 Ratings)

Paperback Dec 2019 352 pages 1st Edition

€8.99 ~~€29.99~~

Renews at €18.99p/m

Rai

€18.99 per month

4.3 (3 Ratings)

Paperback Dec 2019 352 pages 1st Edition

€8.99 ~~€29.99~~

Renews at €18.99p/m

€8.99 ~~€29.99~~

Renews at €18.99p/m

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View table of contents

Preview Book

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

What do you get with a Packt Subscription?

Free for first 7 days. €18.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

View plans & pricing

Product Details

Publication date : Dec 17, 2019

Length: 352 pages

Edition : 1st

Language : English

ISBN-13 : 9781789538779

Category :

Data

Languages :

R

Concepts :

Tools :

H2O

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Table of Contents

19 Chapters

Section 1: Revisiting Deep Learning Basics

Section 1: Revisiting Deep Learning Basics

Revisiting Deep Learning Architecture and Techniques

Revisiting Deep Learning Architecture and Techniques

Deep learning with R

Process of developing a deep network model

Deep learning techniques with R and RStudio

Section 2: Deep Learning for Prediction and Classification

Section 2: Deep Learning for Prediction and Classification

Deep Neural Networks for Multi-Class Classification

Deep Neural Networks for Multi-Class Classification

Cardiotocogram dataset

Preparing the data for model building

Creating and fitting a deep neural network model

Model evaluation and predictions

Performance optimization tips and best practices

Deep Neural Networks for Regression

Deep Neural Networks for Regression

Understanding the Boston Housing dataset

Preparing the data

Creating and fitting a deep neural network model for regression

Model evaluation and prediction

Performance optimization tips and best practices

Section 3: Deep Learning for Computer Vision

Section 3: Deep Learning for Computer Vision

Image Classification and Recognition

Image Classification and Recognition

Handling image data

Data preparation

Creating and fitting the model

Model evaluation and prediction

Performance optimization tips and best practices

Image Classification Using Convolutional Neural Networks

Image Classification Using Convolutional Neural Networks

Data preparation

Layers in the convolutional neural networks

Fitting the model

Model evaluation and prediction

Performance optimization tips and best practices

Applying Autoencoder Neural Networks Using Keras

Applying Autoencoder Neural Networks Using Keras

Types of autoencoders

Dimension reduction autoencoders

Denoising autoencoders

Image correction

Image Classification for Small Data Using Transfer Learning

Image Classification for Small Data Using Transfer Learning

Using a pretrained model to identify an image

Working with the CIFAR10 dataset

Image classification with CNN

Classifying images using the pretrained RESNET50 model

Model evaluation and prediction

Performance optimization tips and best practices

Creating New Images Using Generative Adversarial Networks

Creating New Images Using Generative Adversarial Networks

Generative adversarial network overview

Processing MNIST image data

Developing the generator network

Developing the discriminator network

Training the network

Reviewing results

Performance optimization tips and best practices

Section 4: Deep Learning for Natural Language Processing

Section 4: Deep Learning for Natural Language Processing

Deep Networks for Text Classification

Deep Networks for Text Classification

Preparing the data for model building

Developing deep neural networks

Model evaluation and prediction

Performance optimization tips and best practices

Text Classification Using Recurrent Neural Networks

Text Classification Using Recurrent Neural Networks

Preparing data for model building

Developing a recurrent neural network model

Fitting the model

Model evaluation and prediction

Performance optimization tips and best practices

Text classification Using Long Short-Term Memory Network

Text classification Using Long Short-Term Memory Network

Why do we use LSTM networks?

Preparing text data for model building

Creating a long short-term memory network model

Fitting the LSTM model

Evaluating model performance

Performance optimization tips and best practices

Text Classification Using Convolutional Recurrent Neural Networks

Text Classification Using Convolutional Recurrent Neural Networks

Working with the reuter_50_50 dataset

Preparing the data for model building

Developing the model architecture

Compiling and fitting the model

Evaluating the model and predicting classes

Performance optimization tips and best practices

Section 5: The Road Ahead

Section 5: The Road Ahead

Tips, Tricks, and the Road Ahead

Tips, Tricks, and the Road Ahead

TensorBoard for training performance visualization

Visualizing deep network models with LIME

Visualizing model training with tfruns

Early stopping of network training

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Customer reviews

Rating distribution

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

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

Badshah Mukherjee Apr 06, 2020

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

Silvia Jul 25, 2020

3

Da accompagnare con un altro libro

Amazon Verified review

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About the author

Rai

Rai

Rahul Rai is a technology consultant based in Sydney, Australia with over nine years of professional experience. He has been at the forefront of cloud consulting for government organizations and businesses around the world. Rahul has been working on Microsoft Azure since the service was in its infancy, delivering an ITSM tool built for and on Azure in 2008. Since then, Rahul has played the roles of a developer, a consultant, and an architect for nterprises ranging from small start-ups to multinational corporations. He worked for over five years with Microsoft Services with diverse teams to deliver innovative solutions on Microsoft Azure. In Microsoft, Rahul was a subject matter expert in Microsoft cloud technologies. Rahul has also worked as a cloud solution architect for Microsoft, for which he worked closely with some established Microsoft partners to drive joint customer transformations to cloud based architectures.

See other products by Rai

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