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Advanced Deep Learning with Python

You're reading from   Advanced Deep Learning with Python Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789956177
Length 468 pages
Edition 1st Edition
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Author (1):
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Ivan Vasilev Ivan Vasilev
Author Profile Icon Ivan Vasilev
Ivan Vasilev
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Core Concepts
2. The Nuts and Bolts of Neural Networks FREE CHAPTER 3. Section 2: Computer Vision
4. Understanding Convolutional Networks 5. Advanced Convolutional Networks 6. Object Detection and Image Segmentation 7. Generative Models 8. Section 3: Natural Language and Sequence Processing
9. Language Modeling 10. Understanding Recurrent Networks 11. Sequence-to-Sequence Models and Attention 12. Section 4: A Look to the Future
13. Emerging Neural Network Designs 14. Meta Learning 15. Deep Learning for Autonomous Vehicles 16. Other Books You May Enjoy

Introducing MobileNet

In this section, we'll discuss a lightweight CNN model called MobileNet (MobileNetV2: Inverted Residuals and Linear Bottlenecks, https://arxiv.org/abs/1801.04381). We'll focus on the second revision of this idea (MobileNetV1 was introduced in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, https://arxiv.org/abs/1704.04861).

MobileNet is aimed at devices with limited memory and computing power, such as mobile phones (the name kind of gives it away). To reduce its footprint, the network uses DSC, linear bottlenecks, and inverted residuals.

We are already familiar with DSC, so let's discuss the other two:

  • Linear bottlenecks: To understand this concept, we'll quote the paper:
"Consider a deep neural network consisting of n layers Li each of which has an activation tensor of dimensions . Throughout...
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