finelybook
Deep Learning with R Cookbook: Over 45 unique recipes to delve into neural network techniques using R 3.5.x
by 作者: Swarna Gupta,Rehan Ali Ansari,et al.
pages 页数: 328 pages
Publisher Finelybook 出版社: Packt Publishing (February 21,2020)
Language 语言: English
ISBN-10 书号: 1789805678
ISBN-13 书号: 9781789805673
Book Description
Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries
Deep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs),variational autoencoders (VAEs),and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques.
The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems,and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts,the book features recipes for implementing convolutional neural networks (CNNs),recurrent neural networks (RNNs),and Long short-term memory (LSTMs) networks,as well as sequence-to-sequence models and reinforcement learning. You’ll then learn about high-performance computation using GPUs,along with learning about parallel computation capabilities in R. Later,you’ll explore libraries,such as MXNet,that are designed for GPU computing and state-of-the-art DL. Finally,you’ll discover how to solve different problems in NLP,object detection,and action identification,before understanding how to use pre-trained models in DL apps.
By the end of this book,you’ll have comprehensive knowledge of DL and DL packages,and be able to develop effective solutions for different DL problems.
What you will learn
Work with different datasets for image classification using CNNs
Apply transfer learning to solve complex computer vision problems
Use RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classification
Implement autoencoders for DL tasks such as dimensionality reduction,denoising,and image colorization
Build deep generative models to create photorealistic images using GANs and VAEs
Use MXNet to accelerate the training of DL models through distributed computing
Contents
Preface
Chapter 1: Understanding Neural Networks and Deep Neural Networks
Chapter 2: Working with Convolutional Neural Networks
Chapter 3: Recurrent Neural Networks in Action
Chapter 4: Implementing Autoencoders with Keras
Chapter 5: Deep Generative Models
Chapter 6: Handling Big Data Using Large-Scale Deep Learning
Chapter 7: Working with Text and Audio for NLP
Chapter 8: Deep Learning for Computer Vision
Chapter 9: Implementing Reinforcement Learning
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Index
