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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
Author Profile Icon Luca Massaron
Luca Massaron
Alexia Audevart Alexia Audevart
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Alexia Audevart
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Toc

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

Neural Networks

In this chapter, we will introduce neural networks and how to implement them in TensorFlow. Most of the subsequent chapters will be based on neural networks, so learning how to use them in TensorFlow is very important.

Neural networks are currently breaking records in tasks such as image and speech recognition, reading handwriting, understanding text, image segmentation, dialog systems, autonomous car driving, and so much more. While some of these tasks will be covered in later chapters, it is important to introduce neural networks as a general-purpose, easy-to-implement machine learning algorithm, so that we can expand on it later.

The concept of a neural network has been around for decades. However, it only recently gained traction because we now have the computational power to train large networks because of advances in processing power, algorithm efficiency, and data sizes.

A neural network is, fundamentally, a sequence of operations applied to a matrix...

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