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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
Author Profile Icon Michael Pawlus
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

Contrasting deep learning with machine learning

One key strength of deep learning not shared by other forms of ML is its ability to factor the way variables are related. For instance, if we think back to when we were first learning about animals, then we could imagine a simple task where we are given five images of cats and five images of dogs; later, when we were shown a new image, we would be able to determine whether it was a cat or dog using the patterns that we detected from the previous images that we studied. In our example, it was the images that were to be classified as either cats or dogs. We can consider this example as a training set, and will use the same terminology for the classification of images. Mentally, our brain tries to match the images with the patterns that form the features of these two different species so that we can differentiate between them....

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