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Machine Learning Using TensorFlow Cookbook

You're reading from   Machine Learning Using TensorFlow Cookbook Create powerful machine learning algorithms with TensorFlow

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781800208865
Length 416 pages
Edition 1st Edition
Languages
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Authors (3):
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Konrad Banachewicz Konrad Banachewicz
Author Profile Icon Konrad Banachewicz
Konrad Banachewicz
Luca Massaron Luca Massaron
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Luca Massaron
Alexia Audevart Alexia Audevart
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Alexia Audevart
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Table of Contents (15) Chapters Close

Preface 1. Getting Started with TensorFlow 2.x 2. The TensorFlow Way FREE CHAPTER 3. Keras 4. Linear Regression 5. Boosted Trees 6. Neural Networks 7. Predicting with Tabular Data 8. Convolutional Neural Networks 9. Recurrent Neural Networks 10. Transformers 11. Reinforcement Learning with TensorFlow and TF-Agents 12. Taking TensorFlow to Production 13. Other Books You May Enjoy
14. Index

Parallelizing TensorFlow

Training a model can be very time-consuming. Fortunately, TensorFlow offers several distributed strategies to speed up the training, whether for a very large model or a very large dataset. This recipe will show us how to use the TensorFlow distributed API.

Getting ready

The TensorFlow distributed API allows us to distribute the training by replicating the model into different nodes and training on different subsets of data. Each strategy supports a hardware platform (multiple GPUs, multiple machines, or TPUs) and uses either a synchronous or asynchronous training strategy. In synchronous training, each worker trains over different batches of data and aggregates their gradients at each step. While in the asynchronous mode, each worker is independently training over the data and the variables are updated asynchronously. Note that for the moment, TensorFlow only supports data parallelism described above and according to the roadmap, it will soon...

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