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Hands-On Deep Learning Architectures with Python

You're reading from  Hands-On Deep Learning Architectures with Python

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781788998086
Pages 316 pages
Edition 1st Edition
Languages
Authors (2):
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Profile icon Yuxi (Hayden) Liu
Saransh Mehta Saransh Mehta
Profile icon Saransh Mehta
View More author details
Toc

Table of Contents (15) Chapters close

Preface 1. Section 1: The Elements of Deep Learning
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

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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