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Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles
Uncover the power of artificial neural networks by implementing them through R code.
About This BookDevelop a strong background in neural networks with R, to implement them in your applicationsBuild smart systems using the power of deep learningReal-world case studies to illustrate the power of neural network modelsWho This Book Is ForThis book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need!
What You Will LearnSet up R packages for neural networks and deep learningUnderstand the core concepts of artificial neural networksUnderstand neurons, perceptrons, bias, weights, and activation functionsImplement supervised and unsupervised machine learning in R for neural networksPredict and classify data automatically using neural networksEvaluate and fine-tune the models you build.In DetailNeural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning.
This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you’ll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases.
By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.
Style and approachA step-by-step guide filled with real-world practical examples.
Table of ContentsNeural Network and Artificial Intelligence ConceptsLearning Process in Neural NetworksDeep Learning Using Multilayer Neural NetworksPerceptron Neural Network Modeling – Basic ModelsTraining and Visualizing a Neural Network in RRecurrent and Convolutional Neural NetworksUse Cases of Neural Networks – Advanced Topics
About This BookDevelop a strong background in neural networks with R, to implement them in your applicationsBuild smart systems using the power of deep learningReal-world case studies to illustrate the power of neural network modelsWho This Book Is ForThis book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need!
What You Will LearnSet up R packages for neural networks and deep learningUnderstand the core concepts of artificial neural networksUnderstand neurons, perceptrons, bias, weights, and activation functionsImplement supervised and unsupervised machine learning in R for neural networksPredict and classify data automatically using neural networksEvaluate and fine-tune the models you build.In DetailNeural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning.
This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you’ll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases.
By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.
Style and approachA step-by-step guide filled with real-world practical examples.
Table of ContentsNeural Network and Artificial Intelligence ConceptsLearning Process in Neural NetworksDeep Learning Using Multilayer Neural NetworksPerceptron Neural Network Modeling – Basic ModelsTraining and Visualizing a Neural Network in RRecurrent and Convolutional Neural NetworksUse Cases of Neural Networks – Advanced Topics
272 pages, Kindle Edition
Published September 27, 2017
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October 21, 2018
Great book to get started with Deep Learning using R. This book is much oriented towards letting the users understand the practical basics without much into Mathematical details. For example, you wouldn't find how the loss function works or its mathematical form.
Any way, I have learnt a lot from this book!
Any way, I have learnt a lot from this book!
Displaying 1 of 1 review