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Neural Networks with R

You're reading from   Neural Networks with R Build smart systems by implementing popular deep learning models in R

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781788397872
Length 270 pages
Edition 1st Edition
Languages
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Authors (2):
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Balaji Venkateswaran Balaji Venkateswaran
Author Profile Icon Balaji Venkateswaran
Balaji Venkateswaran
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Toc

Table of Contents (8) Chapters Close

Preface 1. Neural Network and Artificial Intelligence Concepts 2. Learning Process in Neural Networks FREE CHAPTER 3. Deep Learning Using Multilayer Neural Networks 4. Perceptron Neural Network Modeling – Basic Models 5. Training and Visualizing a Neural Network in R 6. Recurrent and Convolutional Neural Networks 7. Use Cases of Neural Networks – Advanced Topics

What is machine learning?


What do we mean by the term machine learning? The definition is quite difficult, to do so, we are asking large field of scientists to help. We can mention an artificial intelligence pioneer's quote:

"Field of study that gives computers the ability to learn without being explicitly programmed." 

 – Arthur Samuel

Machine learning is about training a model or an algorithm with data and then using the model to predict any new data. For example, a toddler is taught how to walk from his crawling phase. Initially, the toddler's parents hold the toddler's hand to help him up, and he is taught through the data that is given. On the basis of these procedures, if an obstacle presents itself in the toddler's path or if there is a turn somewhere, the toddler is able to navigate on his own after the training. The data used for training is the training data and the recipient continues to learn even after the formal training.

Machines too can be taught like toddlers to do a task based...

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Neural Networks with R
Published in: Sep 2017
Publisher: Packt
ISBN-13: 9781788397872
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