Deep Learning with R Cookbook

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.

Type
ebook
Category
Data
publication date
2020-02-21
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

no of pages
328
duration
656
key features
Understand the intricacies of R deep learning packages to perform a range of deep learning tasks * Implement deep learning techniques and algorithms for real-world use cases * Explore various state-of-the-art techniques for fine-tuning neural network models
approach
Each topic provides a compact explanation of the concept and the underlying maths, which helps the readers to grasp the topic easily. It is a ready-to-go recipe-based book to quickly build deep learning models in R with a detailed explanation of codes covering a wide range of applications like image classification, regression, face recognition, object detection, neural machine translation, text summarization, speech recognition and many more.
audience
This deep learning book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to learn key tasks in deep learning domains using a recipe-based approach. A strong understanding of machine learning and working knowledge of the R programming language is mandatory.
meta description
Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries
short description
This book will help you get through the problems that you face during the execution of different tasks and understand hacks in deep learning. With unique recipes, you will implement various deep learning architectures using R 3.5.x. You will cover complex algorithms to perform tasks such as reinforcement learning, GANs, advanced neural networks and more.
subtitle
Over 45 unique recipes to delve into neural network techniques using R 3.5.x
keywords
R, Deep Learning, R 3.X, reinforcement learning, GAN, Neural Network, variational autoencoders, deep reinforcement learning, RNN, LSTM, GPU, algorithms, NLP, object detection, image classification, regression, face recognition, text summarization, speech
Product ISBN
9781789805673