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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

Section 1: Revisiting Deep Learning Basics

This section contains a chapter that serves as an introduction to deep learning with R. It provides an overview of the process for developing deep networks and reviews popular deep learning techniques.

This section contains the following chapter:

  • Chapter 1, Revisiting Deep Learning Architecture and Techniques
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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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 17, 2019
Length: 352 pages
Edition : 1st
Language : English
ISBN-13 : 9781789538779
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Product Details

Publication date : Dec 17, 2019
Length: 352 pages
Edition : 1st
Language : English
ISBN-13 : 9781789538779
Category :
Languages :
Concepts :
Tools :

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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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