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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

MobileNetV2

The second version of MobileNet, referred to as MobileNetV2, is even faster than MobileNet. The second version has fewer parameters as well. Since its launch, MobileNetV2 has been widely used in state-of-the-art object detection and segmentation architectures to make object detection or segmentation possible on devices with limited resources. Let's look at the motivation behind the creation of MobileNetV2.

Motivation behind MobileNetV2

The researchers at Google wanted MobileNet to be even lighter. How can we make MobileNet have fewer parameters? All the CNN-based models increase the number of feature maps (depth channel) while reducing the width and height. One easy way to reduce the network size would be...

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