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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

Product type Course

Published in Dec 2018

Publisher Packt

ISBN-13 9781789957211

Length 764 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Authors (2):

Giuseppe Bonaccorso

Rajalingappaa Shanmugamani

View More author details

Table of Contents (31) Chapters

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

Leave a review - let other readers know what you think

Index

Chapter 23. Object Detection

Object detection is the act of finding the location of an object in an image. In this chapter, we will learn the techniques of object detection and implement pedestrian detection by understanding the following topics:

Basics and the difference between localization and detection
Various datasets and their descriptions
Algorithms used for object localization and detection
TensorFlow API for object detection
Training new object detection models
Pedestrian detection on a moving car with YOLO algorithm

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Authors (2)

Giuseppe Bonaccorso

Giuseppe Bonaccorso

Giuseppe Bonaccorso is an experienced manager in the fields of AI, data science, and machine learning. He has been involved in solution design, management, and delivery in different business contexts. He got his M.Sc.Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata, Italy, and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, neuroscience, and natural language processing.

See other products by Giuseppe Bonaccorso

Rajalingappaa Shanmugamani

Rajalingappaa Shanmugamani

Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

See other products by Rajalingappaa Shanmugamani

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