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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

Detecting objects in an image


Object detection had an explosion concerning both applications and research in recent years. Object detection is a problem of importance in computer vision. Similar to image classification tasks, deeper networks have shown better performance in detection. At present, the accuracy of these techniques is excellent. Hence it used in many applications.

Image classification labels the image as a whole. Finding the position of the object in addition to labeling the object is called object localization. Typically, the position of the object is defined by rectangular coordinates. Finding multiple objects in the image with rectangular coordinates is called detection. Here is an example of object detection:

The image shows four objects with bounding boxes. We will learn algorithms that can perform the task of finding the boxes. The applications are enormous in robot vision, such as self-driving cars and industrial objects. We can summarize localization and detection tasks...

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