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Deep Learning with R Cookbook

Over 45 unique recipes to delve into neural network techniques using R 3.5.x

Author: Swarna Gupta, Published on 21-Feb-2020, Language: English


Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries

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

Book Description

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

Who this book is for

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.

Swarna Gupta

Swarna Gupta holds a B.E. in computer science and has 6 years of experience in the data science space. She is currently working with Rolls Royce in the capacity of a data scientist. Her work revolves around leveraging data science and machine learning to create value for the business. She has extensively worked on IoT-based projects in the vehicle telematics and solar manufacturing industries.During her current association with Rolls Royce she worked in various deep learning techniques and solutions to solve fleet issues in aerospace domain. She also manages time from her busy schedule to be a regular pro-bono contributor to social organizations, helping them to solve specific business problems with the help of data science and machine learning.