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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

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Index

Chapter 17. Distributed Models with TensorFlow Clusters

Previously we learned how to run TensorFlow models at scale in production using Kubernetes, Docker and TensorFlow serving. TensorFlow serving is not the only way to run TensorFlow models at scale. TensorFlow provides another mechanism to not only run but also train the models on different nodes and different devices on multiple nodes or the same node. In this chapter, we shall learn how to distribute the TensorFlow models to run on multiple devices across multiple nodes.

In this chapter, we shall cover the following topics:

Strategies for distributed execution
TensorFlow clusters
Data parallel models
Asynchronous and synchronous updates to distributed models

The rest of the chapter is locked

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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.

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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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