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Deep Learning with TensorFlow 2 and Keras - Second Edition

You're reading from Deep Learning with TensorFlow 2 and Keras - Second Edition

Product type Book

Published in Dec 2019

Publisher Packt

ISBN-13 9781838823412

Pages 646 pages

Edition 2nd Edition

Languages

Concepts

Authors (3):

Antonio Gulli

Amita Kapoor

Sujit Pal

View More author details

Table of Contents (19) Chapters

Preface

1. Neural Network Foundations with TensorFlow 2.0

2. TensorFlow 1.x and 2.x

3. Regression

4. Convolutional Neural Networks

5. Advanced Convolutional Neural Networks

6. Generative Adversarial Networks

7. Word Embeddings

8. Recurrent Neural Networks

9. Autoencoders

10. Unsupervised Learning

11. Reinforcement Learning

12. TensorFlow and Cloud

13. TensorFlow for Mobile and IoT and TensorFlow.js

14. An introduction to AutoML

15. The Math Behind Deep Learning

16. Tensor Processing Unit

17. Other Books You May Enjoy

18. Index

Using TensorFlow 2.1 and nightly build

As of November 2019, you can get full TPU support only with the latest TensorFlow 2.x nightly build. If you use the Google Cloud Console (https://console.cloud.google.com/) you can get the latest nightly build. Just, go to Compute Engine | TPUs | CREATE TPU NODE. The version selector has a "nightly-2.x" option. Martin Görner has a nice demo at http://bit.ly/keras-tpu-tf21 (see Figure 13). This is used for classifying images of flowers:

Figure 13: Martin Görner on Twitter on Full Keras/TPU support

Note that both Regular Keras using model.fit() and custom training loop, distributed are supported. You can refer tohttp://bit.ly/keras-tpu-tf21. Let's look at the most important parts of the code related to TPUs. First at all, the imports:

import re
import tensorflow as tf
import numpy as np
from matplotlib import pyplot as plt
print("Tensorflow version " + tf.__version__)

Then the detection...

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Authors (3)

Antonio Gulli

Antonio Gulli has a passion for establishing and managing global technological talent for innovation and execution. His core expertise is in cloud computing, deep learning, and search engines. Currently, Antonio works for Google in the Cloud Office of the CTO in Zurich, working on Search, Cloud Infra, Sovereignty, and Conversational AI.

See other products by Antonio Gulli

Amita Kapoor

Amita Kapoor is an accomplished AI consultant and educator, with over 25 years of experience. She has received international recognition for her work, including the DAAD fellowship and the Intel Developer Mesh AI Innovator Award. She is a highly respected scholar in her field, with over 100 research papers and several best-selling books on deep learning and AI. After teaching for 25 years at the University of Delhi, Amita took early retirement and turned her focus to democratizing AI education. She currently serves as a member of the Board of Directors for the non-profit Neuromatch Academy, fostering greater accessibility to knowledge and resources in the field. Following her retirement, Amita also founded NePeur, a company that provides data analytics and AI consultancy services. In addition, she shares her expertise with a global audience by teaching online classes on data science and AI at the University of Oxford.

Read more

See other products by Amita Kapoor

Sujit Pal

Sujit Pal is a Technology Research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His interests include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora.

See other products by Sujit Pal

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