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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

Building a perceptron

For now, we will define a perceptron using six specific mathematical representations that demonstrate its learning mechanism. These representations are the inputs, weights, bias term, summation, and the activation function. The output will be functionally elaborate upon here under.

Input

Remember how a biological neuron takes in electrical impulses from its dendrites? Well, the perceptron behaves in a similar fashion, yet it prefers to ingest numbers in lieu of electricity. Essentially, it takes in feature inputs, as shown in the preceding diagram. This particular perceptron only has three input channels, these being x1, x2, and x3. These feature inputs (x1, x2, and x3) can be any independent variable...

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