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Python: Advanced Guide to Artificial Intelligence

You're reading from   Python: Advanced Guide to Artificial Intelligence Expert machine learning systems and intelligent agents using Python

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Product type Course
Published in Dec 2018
Publisher Packt
ISBN-13 9781789957211
Length 764 pages
Edition 1st Edition
Languages
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Authors (2):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
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Toc

Table of Contents (31) Chapters Close

Title Page
About Packt
Contributors
Preface
1. Machine Learning Model Fundamentals FREE CHAPTER 2. Introduction to Semi-Supervised Learning 3. Graph-Based Semi-Supervised Learning 4. Bayesian Networks and Hidden Markov Models 5. EM Algorithm and Applications 6. Hebbian Learning and Self-Organizing Maps 7. Clustering Algorithms 8. Advanced Neural Models 9. Classical Machine Learning with TensorFlow 10. Neural Networks and MLP with TensorFlow and Keras 11. RNN with TensorFlow and Keras 12. CNN with TensorFlow and Keras 13. Autoencoder with TensorFlow and Keras 14. TensorFlow Models in Production with TF Serving 15. Deep Reinforcement Learning 16. Generative Adversarial Networks 17. Distributed Models with TensorFlow Clusters 18. Debugging TensorFlow Models 19. Tensor Processing Units
20. Getting Started 21. Image Classification 22. Image Retrieval 23. Object Detection 24. Semantic Segmentation 25. Similarity Learning 1. Other Books You May Enjoy Index

Localizing algorithms 


Localization algorithms are an extension of the materials learned in Chapter 21, Image Classification and Chapter 22, Image Retrieval. In image classification, an image is passed through several layers of a CNN (convolutional neural network). The final layer of CNN outputs the probabilistic value, belonging to each of the labels. This can be extended to localize the objects. We will see these ideas in the following sections.

Localizing objects using sliding windows

An intuitive way of localization is to predict several cropped portions of an image with an object. The cropping of the images can be done by moving a window across the image and predicting for every window. The method of moving a smaller window than the image and cropping the image according to window size is called a sliding window. A prediction can be made for every cropped window of the image which is called sliding window object detection. 

The prediction can be done by the deep learning model trained...

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