Hands-On Deep Learning with R: A practical guide to designing,building,and improving neural network models using R
by: Michael Pawlus and Rodger Devine
Print Length 页数: 330 pages
Publisher finelybook 出版社: Packt Publishing (24 April 2020)
Language 语言: English
ISBN-10: 1788996836
ISBN-13: 9781788996839
Book Description
By finelybook
Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow,MXNet,H2O,and Deepnet
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.
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
Table of Contents
Preface
Section 1: Deep Learning Basics
Chapter 1: Machine Learning Basics
Chapter 2: Setting Up R for Deep Learning
Chapter 3: Artificial Neural Networks
Section 2: Deep Learning Applications
Chapter 4: CNNs for lmage Recognition
Chapter 5: Multilayer Perceptron for Signal Detection
Chapter 6: Neural Collaborative Filtering Using Embeddings
Chapter 7: Deep Learning for Natural Language Processing
Chapter 8: Long Short-Term Memory Networks for Stock
Forecasting
Chapter 9: Generative Adversarial Networks for Faces
Section 3: Reinforcement Learning
Chapter 10: Reinforcement Learning for Gaming
Chapter 11: Deep Q-Learning for Maze Solving
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Index
