Neural 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 provi
Neural Networks with R (Codes)
β Scribed by Balaji Venkateswaran; Giuseppe Ciaburro
- Publisher
- Packt Publishing
- Year
- 2017
- Tongue
- English
- Leaves
- 270
- Edition
- Codes Only
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Uncover the power of artificial neural networks by implementing them through R code.
About This Book
Develop a strong background in neural networks with R, to implement them in your applications
Build smart systems using the power of deep learning
Real-world case studies to illustrate the power of neural network models
Who This Book Is For
This 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 Learn
Set up R packages for neural networks and deep learning
Understand the core concepts of artificial neural networks
Understand neurons, perceptrons, bias, weights, and activation functions
Implement supervised and unsupervised machine learning in R for neural networks
Predict and classify data automatically using neural networks
Evaluate and fine-tune the models you build.
In Detail
Neural 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 approach
A step-by-step guide filled with real-world practical examples.
π SIMILAR VOLUMES
The book helps you learn neural networks and implement them in R. It covers real-world use cases that will help you better understand their concepts. A basic understanding of R and mathematics is required.
Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the powe
<p><b>Create and unleash the power of neural networks by implementing professional Java code</b></p><h2>About This Book</h2><p>Learn to build amazing projects using neural networks including forecasting the weather and pattern recognition</p><p>Explore the Java multi-platform feature to run your per
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