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Hands-On Deep Learning with R
Hands-On Deep Learning with R

Hands-On Deep Learning with R: A practical guide to designing, building, and improving neural network models using R

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Profile Icon Rodger Devine Profile Icon Pawlus
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NZ$45.99 NZ$51.99
eBook Apr 2020 330 pages 1st Edition
eBook
NZ$45.99 NZ$51.99
Paperback
NZ$64.99
Subscription
Free Trial
Arrow left icon
Profile Icon Rodger Devine Profile Icon Pawlus
Arrow right icon
NZ$45.99 NZ$51.99
eBook Apr 2020 330 pages 1st Edition
eBook
NZ$45.99 NZ$51.99
Paperback
NZ$64.99
Subscription
Free Trial
eBook
NZ$45.99 NZ$51.99
Paperback
NZ$64.99
Subscription
Free Trial

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Hands-On Deep Learning with R

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Key benefits

  • Understand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific problem
  • Improve models using parameter tuning, feature engineering, and ensembling
  • Apply advanced neural network models such as deep autoencoders and generative adversarial networks (GANs) across different domains

Description

Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming. This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You’ll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems. By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms.

Who is this book for?

This book is for data scientists, machine learning engineers, and deep learning developers who are familiar with machine learning and are looking to enhance their knowledge of deep learning using practical examples. Anyone interested in increasing the efficiency of their machine learning applications and exploring various options in R will also find this book useful. Basic knowledge of machine learning techniques and working knowledge of the R programming language is expected.

What you will learn

  • Design a feedforward neural network to see how the activation function computes an output
  • Create an image recognition model using convolutional neural networks (CNNs)
  • Prepare data, decide hidden layers and neurons and train your model with the backpropagation algorithm
  • Apply text cleaning techniques to remove uninformative text using NLP
  • Build, train, and evaluate a GAN model for face generation
  • Understand the concept and implementation of reinforcement learning in R

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 24, 2020
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781788993784
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Google
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What do you get with eBook?

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Product Details

Publication date : Apr 24, 2020
Length: 330 pages
Edition : 1st
Language : English
ISBN-13 : 9781788993784
Vendor :
Google
Category :
Languages :
Concepts :
Tools :

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Frequently bought together


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Table of Contents

15 Chapters
Section 1: Deep Learning Basics Chevron down icon Chevron up icon
Machine Learning Basics Chevron down icon Chevron up icon
Setting Up R for Deep Learning Chevron down icon Chevron up icon
Artificial Neural Networks Chevron down icon Chevron up icon
Section 2: Deep Learning Applications Chevron down icon Chevron up icon
CNNs for Image Recognition Chevron down icon Chevron up icon
Multilayer Perceptron for Signal Detection Chevron down icon Chevron up icon
Neural Collaborative Filtering Using Embeddings Chevron down icon Chevron up icon
Deep Learning for Natural Language Processing Chevron down icon Chevron up icon
Long Short-Term Memory Networks for Stock Forecasting Chevron down icon Chevron up icon
Generative Adversarial Networks for Faces Chevron down icon Chevron up icon
Section 3: Reinforcement Learning Chevron down icon Chevron up icon
Reinforcement Learning for Gaming Chevron down icon Chevron up icon
Deep Q-Learning for Maze Solving Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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