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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

Evolution path to MobileNets

CNNs present a promising future for computer vision. CNNs have laid out a benchmark for complex computer vision tasks such as detection and recognition with their remarkable performance in the ILSVRC competition over consecutive years. But the computation power required by these CNN models has always been quite high. This could lead to a major setback for the commercial use of CNNs. Almost all object detection-related tasks in the real world are performed through portable devices, such as mobile phones, surveillance cameras, or any other embedded device. These devices have limited computational abilities and memory. To make any deep learning network running on a portable device, the network weights and the number of calculations occurring in the network (that is, the number of parameters in the network) have to very small. CNNs have millions of...

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