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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 FREE CHAPTER 2. Learning Process in Neural Networks 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

MNIST HWR using R


Handwriting Recognition (HWR) is a very commonly used procedure in modern technology. The image of the written text can be detected offline from a piece of paper by optical scanning (optical character recognition (OCR)) or intelligent word recognition. Alternatively, pen tip movements can be detected online (for example, from a pen-computer surface, a task that is generally easier since there are more clues available). Technically, recognition of handwriting is the ability of a computer to receive and interpret a handwritten intelligible input from sources such as paper documents, photos, touchscreens, and other devices.

HWR is performed through various techniques that generally require OCR. However, a complete script recognition system also manages formatting, carries out correct character segmentation, and finds the most plausible words.

Modified National Institute of Standards and Technology (MNIST) is a large database of handwritten digits. It has a set of 70,000 examples...

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