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You're reading from TensorFlow 2.0 Quick Start Guide Get up to speed with the newly introduced features of TensorFlow 2.0

Product type Paperback

Published in Mar 2019

Publisher Packt

ISBN-13 9781789530759

Length 196 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Author (1):

Tony Holdroyd

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Table of Contents (15) Chapters

Preface

1. Section 1: Introduction to TensorFlow 2.00 Alpha

2. Introducing TensorFlow 2 FREE CHAPTER

3. Keras, a High-Level API for TensorFlow 2

4. ANN Technologies Using TensorFlow 2

5. Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha

6. Supervised Machine Learning Using TensorFlow 2

7. Unsupervised Learning Using TensorFlow 2

8. Section 3: Neural Network Applications of TensorFlow 2.00 Alpha

9. Recognizing Images with TensorFlow 2

10. Neural Style Transfer Using TensorFlow 2

11. Recurrent Neural Networks Using TensorFlow 2

12. TensorFlow Estimators and TensorFlow Hub

13. Converting from tf1.12 to tf2

14. Other Books You May Enjoy

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TensorFlow Hub

TensorFlow Hub is a software library. Its purpose is to provide reusable components, known as modules, that can be leveraged in contexts other than the original context in which they were developed. By a module, we mean a self-contained piece of a TensorFlow graph, along with its weights, which can be reused across other, similar tasks.

IMDb (database of movie reviews)

In this section, we will examine an application based on one from Google that analyzes a subset of the IMDb of movie reviews in what is termed sentiment analysis. The subset is hosted by Stanford and contains reviews of each movie, together with a sentiment on a positivity scale of 1 to 4 (bad) and 7 to 10 (good). The problem is determining the...

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Authors (1)

Holdroyd

Tony Holdroyd's first degree, from Durham University, was in maths and physics. He also has technical qualifications, including MCSD, MCSD.net, and SCJP. He holds an MSc in computer science from London University. He was a senior lecturer in computer science and maths in further education, designing and delivering programming courses in many languages, including C, C+, Java, C#, and SQL. His passion for neural networks stems from research he did for his MSc thesis. He has developed numerous machine learning, neural network, and deep learning applications, and has advised in the media industry on deep learning as applied to image and music processing. Tony lives in Gravesend, Kent, UK, with his wife, Sue McCreeth, who is a renowned musician.

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