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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
2. Getting Started with Deep Learning FREE CHAPTER 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

Deep learning

Though machine learning has provided computers with the capability to learn decision boundaries, it misses out on the robustness of doing so. Machine learning models have to be very specifically designed for every particular application. People spent hours deciding what features to select for optimal learning. As the data cross folded and non-linearity in data increased, machine learning models struggled to produce accurate results. Scientists soon realized that a much more powerful tool was required to apex this growth. In the 1980s, the concept of ANN was reborn, and with faster computing capabilities, deeper versions of ANN were developed, providing us with the powerful tool we were looking for—deep learning!

Applications of deep learning

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You have been reading a chapter from
Hands-On Deep Learning Architectures with Python
Published in: Apr 2019
Publisher: Packt
ISBN-13: 9781788998086
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