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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

Product type Paperback

Published in Feb 2021

Publisher Packt

ISBN-13 9781800208865

Length 416 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Authors (3):

Konrad Banachewicz

Luca Massaron

Alexia Audevart

View More author details

Table of Contents (15) Chapters

Preface

1. Getting Started with TensorFlow 2.x

2. The TensorFlow Way FREE CHAPTER

3. Keras

4. Linear Regression

5. Boosted Trees

Introduction

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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Authors (3)

Alexia Audevart

Alexia Audevart

Alexia Audevart, also a Google Developer Expert in machine learning, is the founder of datactik. She is a data scientist and helps her clients solve business problems by making their applications smarter. Her first book is a collaboration on artificial intelligence and neuroscience.

See other products by Alexia Audevart

Konrad Banachewicz

Konrad Banachewicz

Konrad Banachewicz is the author of the bestselling, The Kaggle Book and The Kaggle Workbook. He is a data science manager with experience stretching longer than he likes to ponder on. He holds a PhD in statistics from Vrije Universiteit Amsterdam, where he focused on problems of extreme dependency modeling in credit risk. He slowly moved from classic statistics towards machine learning and into the business applications world.

See other products by Konrad Banachewicz

Luca Massaron

Luca Massaron

Having joined Kaggle over 10 years ago, Luca Massaron is a Kaggle Grandmaster in discussions and a Kaggle Master in competitions and notebooks. In Kaggle competitions he reached no. 7 in the worldwide rankings. On the professional side, Luca is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is a Google Developer Expert(GDE) in machine learning and the author of best-selling books on AI, machine learning, and algorithms.

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