9781838642709-1838642706-Deep Learning with R for Beginners: Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

Deep Learning with R for Beginners: Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

ISBN-13: 9781838642709
ISBN-10: 1838642706
Author: Hodnett, Mark, Wiley, Joshua F., Liu, Yuxi (Hayden), Maldonado, Pablo
Publication date: 2019
Publisher: Packt Publishing
Format: Paperback 612 pages
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Book details

ISBN-13: 9781838642709
ISBN-10: 1838642706
Author: Hodnett, Mark, Wiley, Joshua F., Liu, Yuxi (Hayden), Maldonado, Pablo
Publication date: 2019
Publisher: Packt Publishing
Format: Paperback 612 pages

Summary

Acknowledged authors Hodnett, Mark, Wiley, Joshua F., Liu, Yuxi (Hayden), Maldonado, Pablo wrote Deep Learning with R for Beginners: Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet comprising 612 pages back in 2019. Textbook and eTextbook are published under ISBN 1838642706 and 9781838642709. Since then Deep Learning with R for Beginners: Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.

Description

Explore the world of neural networks by building powerful deep learning models using the R ecosystem

Key Features
  • Get to grips with the fundamentals of deep learning and neural networks
  • Use R 3.5 and its libraries and APIs to build deep learning models for computer vision and text processing
  • Implement effective deep learning systems in R with the help of end-to-end projects
Book Description

Deep learning finds practical applications in several domains, while R is the preferred language for designing and deploying deep learning models.

This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you'll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The book will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you'll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R.

By the end of this Learning Path, you'll be well versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.

This Learning Path includes content from the following Packt products:

  • R Deep Learning Essentials - Second Edition by Joshua F. Wiley and Mark Hodnett
  • R Deep Learning Projects by Yuxi (Hayden) Liu and Pablo Maldonado
What you will learn
  • Implement credit card fraud detection with autoencoders
  • Train neural networks to perform handwritten digit recognition using MXNet
  • Reconstruct images using variational autoencoders
  • Explore the applications of autoencoder neural networks in clustering and dimensionality reduction
  • Create natural language processing (NLP) models using Keras and TensorFlow in R
  • Prevent models from overfitting the data to improve generalizability
  • Build shallow neural network prediction models
Who this book is for

This Learning Path is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. A fundamental understanding of R programming and familiarity with the basic concepts of deep learning are necessary to get the most out of this Learning Path.

Table of Contents
  1. Getting Started with Deep Learning
  2. Training a Prediction Model
  3. Deep Learning Fundamentals
  4. Training Deep Prediction Models
  5. Image Classification Using Convolutional Neural Networks
  6. Tuning and Optimizing Models
  7. Natural Language Processing Using Deep Learning
  8. Deep Learning Models Using TensorFlow in R
  9. Anomaly Detection and Recommendation Systems
  10. Running Deep Learning Models in the Cloud
  11. The Next Level in Deep Learning
  12. Handwritten Digit Recognition Using Convolutional Neural Networks
  13. Traffic Sign Recognition for Intelligent Vehicles
  14. Fraud Detection with Autoencoders
  15. Text Generation Using Recurrent Neural Networks
  16. Sentiment Analysis with Word Embeddings
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