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Deep Learning with TensorFlow and Keras – 3rd edition - Third Edition

You're reading from Deep Learning with TensorFlow and Keras – 3rd edition - Third Edition

Product type Book

Published in Oct 2022

Publisher Packt

ISBN-13 9781803232911

Pages 698 pages

Edition 3rd Edition

Languages

Concepts

Authors (3):

Amita Kapoor

Antonio Gulli

Sujit Pal

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Table of Contents (23) Chapters

Preface

1. Neural Network Foundations with TF

2. Regression and Classification

3. Convolutional Neural Networks

4. Word Embeddings

5. Recurrent Neural Networks

6. Transformers

7. Unsupervised Learning

8. Autoencoders

9. Generative Models

10. Self-Supervised Learning

11. Reinforcement Learning

12. Probabilistic TensorFlow

13. An Introduction to AutoML

14. The Math Behind Deep Learning

15. Tensor Processing Unit

16. Other Useful Deep Learning Libraries

17. Graph Neural Networks

18. Machine Learning Best Practices

19. TensorFlow 2 Ecosystem

20. Advanced Convolutional Neural Networks

21. Other Books You May Enjoy

22. Index

TensorFlow Probability

TensorFlow Probability (TFP), a part of the TensorFlow ecosystem, is a library that provides tools for developing probabilistic models. It can be used to perform probabilistic reasoning and statistical analysis. It is built over TensorFlow and provides the same computational advantage.

Figure 12.1 shows the major components constituting TensorFlow Probability:

Graphical user interface, application Description automatically generated

Figure 12.1: Different components of TensorFlow Probability

At the root, we have all numerical operations supported by TensorFlow, specifically the LinearOperator class (part of tf.linalg) – it contains all the methods that can be performed on a matrix, without the need to actually materialize the matrix. This provides computationally efficient matrix-free computations. TFP includes a large collection of probability distributions and their related statistical computations. It also has tfp.bijectors, which offers a wide range of transformed distributions.

Bijectors encapsulate...

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Authors (3)

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.

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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.

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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.

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