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Hands-On Neural Networks with Keras

You're reading from   Hands-On Neural Networks with Keras Design and create neural networks using deep learning and artificial intelligence principles

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789536089
Length 462 pages
Edition 1st Edition
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Author (1):
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Niloy Purkait Niloy Purkait
Author Profile Icon Niloy Purkait
Niloy Purkait
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Table of Contents (16) Chapters Close

Preface 1. Section 1: Fundamentals of Neural Networks FREE CHAPTER
2. Overview of Neural Networks 3. A Deeper Dive into Neural Networks 4. Signal Processing - Data Analysis with Neural Networks 5. Section 2: Advanced Neural Network Architectures
6. Convolutional Neural Networks 7. Recurrent Neural Networks 8. Long Short-Term Memory Networks 9. Reinforcement Learning with Deep Q-Networks 10. Section 3: Hybrid Model Architecture
11. Autoencoders 12. Generative Networks 13. Section 4: Road Ahead
14. Contemplating Present and Future Developments 15. Other Books You May Enjoy

Understanding pooling layers

A final consideration when using convolutional layers is to do with the idea of stacking simple cells to detect local patterns and complex cells to downsample representations, as we saw earlier with the cat-brain experiments, and the neocognitron. The convolutional filters we saw behave like simple cells by focusing on specific locations on the input and training neurons to fire, given some stimuli from the local regions of our input image. Complex cells, on the other hand, are required to be less specific to the location of the stimuli. This is where the pooling layer comes in. This technique of pooling intends to reduce the output of CNN layers to more manageable representations. Pooling layers are periodically added between convolutional layers to spatially downsample the outputs of our convolutional layer. All this does is progressively reduce...

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