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Hands-On Deep Learning Architectures with Python

You're reading from   Hands-On Deep Learning Architectures with Python Create deep neural networks to solve computational problems using TensorFlow and Keras

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788998086
Length 316 pages
Edition 1st Edition
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Authors (2):
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Saransh Mehta Saransh Mehta
Author Profile Icon Saransh Mehta
Saransh Mehta
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: The Elements of Deep Learning FREE CHAPTER
2. Getting Started with Deep Learning 3. Deep Feedforward Networks 4. Restricted Boltzmann Machines and Autoencoders 5. Section 2: Convolutional Neural Networks
6. CNN Architecture 7. Mobile Neural Networks and CNNs 8. Section 3: Sequence Modeling
9. Recurrent Neural Networks 10. Section 4: Generative Adversarial Networks (GANs)
11. Generative Adversarial Networks 12. Section 5: The Future of Deep Learning and Advanced Artificial Intelligence
13. New Trends of Deep Learning 14. Other Books You May Enjoy

Building the fundamentals

This section is where you will begin the journey of being a deep learning architect. Deep learning stands on the pillar of ANNs. Our first step should be to understand how they work. In this section, we describe the biological inspiration behind the artificial neuron and the mathematical model to create an ANN. We have tried keeping the mathematics to a minimum and focused more on concepts. However, we assume you are familiar with basic algebra and calculus.

Biological inspiration 

As we mentioned earlier, deep learning is inspired by the human brain. This seems a good idea indeed. To develop the intelligence of the brain inside a machine, you need the machine to mimic the brain! Now, if you...

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