Neural Networks with R Neural Networks with R

Neural Networks with R

    • $39.99
    • $39.99

Publisher Description

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 applicationsBuild smart systems using the power of deep learningReal-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 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 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.

GENRE
Computing & Internet
RELEASED
2017
27 September
LANGUAGE
EN
English
LENGTH
270
Pages
PUBLISHER
Packt Publishing
SELLER
PublishDrive Inc.
SIZE
15.7
MB

More Books Like This

The Deep Learning with Keras Workshop The Deep Learning with Keras Workshop
2020
Hands-on Machine Learning with Python Hands-on Machine Learning with Python
2022
TensorFlow Machine Learning Projects TensorFlow Machine Learning Projects
2018
Beginning with Machine Learning: The Ultimate Introduction to Machine Learning, Deep Learning, Scikit-learn, and TensorFlow (English Edition) Beginning with Machine Learning: The Ultimate Introduction to Machine Learning, Deep Learning, Scikit-learn, and TensorFlow (English Edition)
2022
Mastering TensorFlow 2.x: Implement Powerful Neural Nets across Structured, Unstructured datasets and Time Series Data (English Edition) Mastering TensorFlow 2.x: Implement Powerful Neural Nets across Structured, Unstructured datasets and Time Series Data (English Edition)
2022
Mastering Predictive Analytics with scikit-learn and TensorFlow Mastering Predictive Analytics with scikit-learn and TensorFlow
2018

More Books by Giuseppe Ciaburro & Balaji Venkateswaran

Hands-On Data Warehousing with Azure Data Factory Hands-On Data Warehousing with Azure Data Factory
2018
Keras Reinforcement Learning Projects Keras Reinforcement Learning Projects
2018
Hands-On Simulation Modeling with Python, Hands-On Simulation Modeling with Python,
2022
MATLAB for Machine Learning MATLAB for Machine Learning
2024
Keras 2.x Projects Keras 2.x Projects
2018
Hands-On Machine Learning on Google Cloud Platform Hands-On Machine Learning on Google Cloud Platform
2018