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Deep Learning with TensorFlow and Keras – 3rd edition

You're reading from Deep Learning with TensorFlow and Keras – 3rd edition Build and deploy supervised, unsupervised, deep, and reinforcement learning models

Product type Paperback

Published in Oct 2022

Publisher Packt

ISBN-13 9781803232911

Length 698 pages

Edition 3rd Edition

Tools

Keras

Concepts

Deep Learning

Authors (3):

Sujit Pal

Antonio Gulli

Dr. Amita Kapoor

View More author details

Table of Contents (23) Chapters

Preface

1. Neural Network Foundations with TF

2. Regression and Classification FREE CHAPTER

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

Amita Kapoor, a seasoned expert in Artificial Intelligence, serves as the Chief Artificial Intelligence Officer at TIPZ AI, bringing over 25 years of experience in AI, data science, and neuroscience. Her consultancy, NePeur, stands testament to her leadership in applying AI across diverse industries, enhancing operational efficiency and business intelligence. Amita is also a devoted board member of Neuromatch Academy, where she plays a crucial role in making neuroscience and deep learning education accessible to all. Holding a PhD from the University of Delhi, she has dedicated her career to education, authoring numerous articles and papers, and creating impactful online classes. Her significant contributions extend to pioneering projects in intelligent vehicle fleet management, home surveillance through AI-powered face detection, and robust data anonymization solutions. Connect with Amita on LinkedIn.

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Sujit Pal

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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Antonio Gulli

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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