Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788998086
Length 316 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
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
Arrow right icon
View More author details
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

SSD MobileNetV2

The makers of MobileNetV2 also made real-time object detection possible for mobile devices. They introduced a combination of the SSD Object Detector and MobileNetV2, which is called SSDLite. Remember that in Chapter 4CNN Architecture, we used ssd_mobilenetv2 for object detection. It is the same as SSDLite. The reason for choosing SSD is quite simple. SSD is built independent of the base network and hence the convolutions are replaced by depth-wise separable convolution. The first layer of SSDLite is attached to the expansion of layer 15 of MobileNetV2. Replacing standard convolutions with depth-wise separable convolution significantly reduces the number of parameters that are required by the network for object detection.

The following table shows a comparison of the number of parameters and multiplication operations required by the original SSD network...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image