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Hands-On Deep Learning with R

You're reading from   Hands-On Deep Learning with R A practical guide to designing, building, and improving neural network models using R

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781788996839
Length 330 pages
Edition 1st Edition
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Authors (2):
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Rodger Devine Rodger Devine
Author Profile Icon Rodger Devine
Rodger Devine
Michael Pawlus Michael Pawlus
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Michael Pawlus
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Table of Contents (16) Chapters Close

Preface 1. Section 1: Deep Learning Basics
2. Machine Learning Basics FREE CHAPTER 3. Setting Up R for Deep Learning 4. Artificial Neural Networks 5. Section 2: Deep Learning Applications
6. CNNs for Image Recognition 7. Multilayer Perceptron for Signal Detection 8. Neural Collaborative Filtering Using Embeddings 9. Deep Learning for Natural Language Processing 10. Long Short-Term Memory Networks for Stock Forecasting 11. Generative Adversarial Networks for Faces 12. Section 3: Reinforcement Learning
13. Reinforcement Learning for Gaming 14. Deep Q-Learning for Maze Solving 15. Other Books You May Enjoy

Image recognition with shallow nets

Image classifiers can be created without using deep-learning algorithms and methods. To demonstrate, let's use the Fashion MNIST dataset, which is an alternative to the MNIST handwriting dataset. The name MNIST stands for the Modified National Institute of Standards and Technology database, and as the name suggests, it is a modified version of the original dataset created by the National Institute of Standards and Technology. While MNIST is a series of hand-drawn numbers, Fashion MNIST uses small images of different types of clothing. The clothing in the dataset is labeled with one of ten categories. Fashion MNIST has nothing to do with the National Institute of Standards and Technology; however, the MNIST name carried over since it is well-known as a database to use for image recognition.

Since this dataset is not very...

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