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- Deep Learning with R Cookbook. Over 45 unique recipes to delve into neural network techniques using R 3.5.x
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Deep Learning with R Cookbook. Over 45 unique recipes to delve into neural network techniques using R 3.5.x
Swarna Gupta, Rehan Ali Ansari, Dipayan Sarkar
Ebook
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.
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.
- 1. Understanding Neural Networks and Deep Neural Networks
- 2. Working with Convolutional Neural Network
- 3. Recurrent Neural Networks in Action
- 4. Implementing Autoencoders with Keras
- 5. Deep Generative Models
- 6. Handling Big Data Using Large-Scale Deep Learning
- 7. Working with Text and Audio for NLP
- 8. Deep Learning for Computer Vision
- 9. Implementing Reinforcement Learning
- Title: Deep Learning with R Cookbook. Over 45 unique recipes to delve into neural network techniques using R 3.5.x
- Author: Swarna Gupta, Rehan Ali Ansari, Dipayan Sarkar
- Original title: Deep Learning with R Cookbook. Over 45 unique recipes to delve into neural network techniques using R 3.5.x
- ISBN: 9781789808278, 9781789808278
- Date of issue: 2020-02-21
- Format: Ebook
- Item ID: e_2ab6
- Publisher: Packt Publishing