Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Free Learning

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

€8.99 ~~€29.99~~

4.3 (3 Ratings)

eBook Dec 2019 352 pages 1st Edition

€8.99 ~~€29.99~~

Renews at €18.99p/m

Rai

€8.99 ~~€29.99~~

4.3 (3 Ratings)

eBook Dec 2019 352 pages 1st Edition

€8.99 ~~€29.99~~

Renews at €18.99p/m

€8.99 ~~€29.99~~

Renews at €18.99p/m

What do you get with eBook?

Instant access to your Digital eBook purchase

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

OR

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 eBook?

Instant access to your Digital eBook purchase

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

OR

Product Details

Publication date : Dec 17, 2019

Length: 352 pages

Edition : 1st

Language : English

ISBN-13 : 9781789534986

Category :

Data

Languages :

R

Concepts :

Tools :

H2O

Frequently bought together

Advanced Deep Learning with R

Advanced Deep Learning with R

Dec 2019 352 pages

eBook

€8.99 ~~€29.99~~

Advanced Deep Learning with Python

Advanced Deep Learning with Python

Dec 2019 468 pages

eBook

€8.99 ~~€29.99~~

Hands-On Time Series Analysis with R

Hands-On Time Series Analysis with R

May 2019 448 pages

eBook

€8.99 ~~€23.99~~

Total € 103.97

Advanced Deep Learning with R

€36.99

Advanced Deep Learning with Python

€36.99

Hands-On Time Series Analysis with R

€29.99

Total € 103.97

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

Other Books You May Enjoy

Other Books You May Enjoy

Leave a review - let other readers know what you think

Recommendations for you

Python Machine Learning By Example

Python Machine Learning By Example

Jul 2024 518 pages

eBook

€8.99 ~~€27.99~~

Unlocking Data with Generative AI and RAG

Unlocking Data with Generative AI and RAG

Sep 2024 346 pages

eBook

€8.99 ~~€23.99~~

Building LLM Powered Applications

Building LLM Powered Applications

May 2024 342 pages

eBook

€8.99 ~~€29.99~~

Python for Algorithmic Trading Cookbook

Python for Algorithmic Trading Cookbook

Aug 2024 404 pages

eBook

€8.99 ~~€35.99~~

Machine Learning with PyTorch and Scikit-Learn

Machine Learning with PyTorch and Scikit-Learn

Feb 2022 774 pages

eBook

€8.99 ~~€32.99~~

Artificial Intelligence Engines

Artificial Intelligence Engines

Nov 2024 217 pages

eBook

€4.99 ~~€7.99~~

AI Product Manager's Handbook

AI Product Manager's Handbook

Nov 2024 484 pages

eBook

€8.99 ~~€29.99~~

RAG-Driven Generative AI

RAG-Driven Generative AI

Sep 2024 334 pages

eBook

€8.99 ~~€32.99~~

Hands-On Reinforcement Learning with Python

Hands-On Reinforcement Learning with Python

Jun 2018 318 pages

eBook

€8.99 ~~€23.99~~

LLM Engineer's Handbook

LLM Engineer's Handbook

Oct 2024 522 pages

eBook

€8.99 ~~€43.99~~

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

People who bought this also bought

Machine Learning Engineering with Python

Machine Learning Engineering with Python

Aug 2023 462 pages

eBook

€8.99 ~~€29.99~~

Deep Learning with TensorFlow and Keras – 3rd edition

Deep Learning with TensorFlow and Keras – 3rd edition

Oct 2022 698 pages

eBook

€8.99 ~~€29.99~~

Modern Generative AI with ChatGPT and OpenAI Models

Modern Generative AI with ChatGPT and OpenAI Models

May 2023 286 pages

eBook

€8.99 ~~€29.99~~

Generative AI with LangChain

Generative AI with LangChain

Dec 2023 368 pages

eBook

€8.99 ~~€29.99~~

Causal Inference and Discovery in Python

Causal Inference and Discovery in Python

May 2023 456 pages

eBook

€8.99 ~~€23.99~~

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

FAQs

How do I buy and download an eBook?

Where there is an eBook version of a title available, you can buy it from the book details for that title. Add either the standalone eBook or the eBook and print book bundle to your shopping cart. Your eBook will show in your cart as a product on its own. After completing checkout and payment in the normal way, you will receive your receipt on the screen containing a link to a personalised PDF download file. This link will remain active for 30 days. You can download backup copies of the file by logging in to your account at any time.

If you already have Adobe reader installed, then clicking on the link will download and open the PDF file directly. If you don't, then save the PDF file on your machine and download the Reader to view it.

Please Note: Packt eBooks are non-returnable and non-refundable.

Packt eBook and Licensing When you buy an eBook from Packt Publishing, completing your purchase means you accept the terms of our licence agreement. Please read the full text of the agreement. In it we have tried to balance the need for the ebook to be usable for you the reader with our needs to protect the rights of us as Publishers and of our authors. In summary, the agreement says:

You may make copies of your eBook for your own use onto any machine
You may not pass copies of the eBook on to anyone else

How can I make a purchase on your website?

If you want to purchase a video course, eBook or Bundle (Print+eBook) please follow below steps:

Register on our website using your email address and the password.
Search for the title by name or ISBN using the search option.
Select the title you want to purchase.
Choose the format you wish to purchase the title in; if you order the Print Book, you get a free eBook copy of the same title.
Proceed with the checkout process (payment to be made using Credit Card, Debit Cart, or PayPal)

Where can I access support around an eBook?

If you experience a problem with using or installing Adobe Reader, the contact Adobe directly.
To view the errata for the book, see www.packtpub.com/support and view the pages for the title you have.
To view your account details or to download a new copy of the book go to www.packtpub.com/account
To contact us directly if a problem is not resolved, use www.packtpub.com/contact-us

What eBook formats do Packt support?

Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks?

You can get the information you need immediately
You can easily take them with you on a laptop
You can download them an unlimited number of times
You can print them out
They are copy-paste enabled
They are searchable
There is no password protection
They are lower price than print
They save resources and space

What is an eBook?

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.