As the first use case of RNNs, we see how we can train and predict an RNN using the trainr() function. Our purpose is to forecast the humidity of a certain location as a function of the day. The input file contains daily weather observations from multiple Australian weather stations. These observations are obtained from the Australian Commonwealth Bureau of Meteorology and are subsequently processed to create a relatively large sample dataset for illustrating analytics, data mining, and data science using R and the rattle.data package. The weatherAUS dataset is regularly updated and updates of this package usually correspond to updates to this dataset. The data is updated from the Bureau of Meteorology website. The locationsAUS dataset records the location of each weather station. The source dataset is copyrighted by the Australian Commonwealth Bureau of Meteorology and is used with permission.
Neural Networks with R
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Neural Networks with R
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Overview of this book
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.
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Current Title:
Neural Networks with R
Table of Contents (14 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
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Preface
Free Chapter
Neural Network and Artificial Intelligence Concepts
Learning Process in Neural Networks
Deep Learning Using Multilayer Neural Networks
Perceptron Neural Network Modeling – Basic Models
Training and Visualizing a Neural Network in R
Recurrent and Convolutional Neural Networks
Use Cases of Neural Networks – Advanced Topics
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