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Advanced Deep Learning with TensorFlow 2 and Keras

You're reading from   Advanced Deep Learning with TensorFlow 2 and Keras Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781838821654
Length 512 pages
Edition 2nd Edition
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Author (1):
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Rowel Atienza Rowel Atienza
Author Profile Icon Rowel Atienza
Rowel Atienza
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Table of Contents (16) Chapters Close

Preface 1. Introducing Advanced Deep Learning with Keras 2. Deep Neural Networks FREE CHAPTER 3. Autoencoders 4. Generative Adversarial Networks (GANs) 5. Improved GANs 6. Disentangled Representation GANs 7. Cross-Domain GANs 8. Variational Autoencoders (VAEs) 9. Deep Reinforcement Learning 10. Policy Gradient Methods 11. Object Detection 12. Semantic Segmentation 13. Unsupervised Learning Using Mutual Information 14. Other Books You May Enjoy
15. Index

Object Detection

Object detection is one of the most important applications of computer vision. Object detection is the task of simultaneous localization and identification of an object that is present in an image. For autonomous vehicles to safely navigate the streets, the algorithm must detect the presence of pedestrians, roads, vehicles, traffic lights, signs, and unexpected obstacles. In security, the presence of an intruder can be used to trigger an alarm or inform the appropriate authorities.

Though important, object detection has been a long-standing problem in computer vision. Many algorithms have been proposed but are generally slow, with low precision and recall. Similar to what AlexNet [1] has achieved in the ImageNet large-scale image classification problem, deep learning has significantly advanced the area of object detection. State-of-the-art object detection methods can now run in real time and have a much higher precision and recall.

In this chapter, we...

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