Advanced Deep Learning with R: Become an expert at designing,building,and improving advanced neural network models using R
Authors: Bharatendra Rai
ISBN-10: 1789538777
ISBN-13: 9781789538779
Publication Date 出版日期: 2019-12-17
Print Length 页数: 352 pages
Book Description
By finelybook
Discover best practices for choosing,building,training,and improving deep learning models using Keras-R,and TensorFlow-R libraries
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.
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
Contents
Preface
Section 1: Revisiting Deep Learning Basics
Chapter 1: Revisiting Deep Learning Architecture and Techniques
Section 2: Deep Learning for Prediction and Classification
Chapter 2: Deep Neural Networks for Multi-Class Classification
Chapter 3: Deep Neural Networks for Regression
Section 3: Deep Learning for Computer Vision
Chapter 4: Image Classification and Recognition
Chapter 5: Image Classification Using Convolutional Neural Networks
Chapter 6: Applying Autoencoder Neural Networks Using Keras
Chapter 7: Image Classification for Small Data Using Transfer Learning
Chapter 8: Creating New Images Using Generative Adversarial Networks
Section 4: Deep Learning for Natural Language Processing
Chapter 9: Deep Networks for Text Classification
Chapter 10: Text Classification Using Recurrent Neural Networks
Chapter 11: Text classification Using Long Short-Term Memory Network
Chapter 12: Text Classification Using Convolutional Recurrent Neural Networks
Section 5: The Road Ahead
Chapter 13: Tips,Tricks,and the Road Ahead
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Index
