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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

Neural Network Foundations with TensorFlow 2.0

TensorFlow 1.x and 2.x

Regression

Convolutional Neural Networks

Advanced Convolutional Neural Networks

Generative Adversarial Networks

Word Embeddings

Recurrent Neural Networks

Autoencoders

Unsupervised Learning

Reinforcement Learning

TensorFlow and Cloud

TensorFlow for Mobile and IoT and TensorFlow.js

An introduction to AutoML

The Math Behind Deep Learning

Tensor Processing Unit

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Index

References

F. Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain, Psychol. Rev., vol. 65, pp. 386–408, Nov. 1958.
P. J. Werbos, Backpropagation through time: what it does and how to do it, Proc. IEEE, vol. 78, pp. 1550–1560, 1990.
G. E. Hinton, S. Osindero, and Y.-W. Teh, A fast learning algorithm for deep belief nets, Neural Comput., vol. 18, pp. 1527–1554, 2006.
J. Schmidhuber, Deep Learning in Neural Networks: An Overview, Neural networks : Off. J. Int. Neural Netw. Soc., vol. 61, pp. 85–117, Jan. 2015.
S. Leven, The roots of backpropagation: From ordered derivatives to neural networks and political forecasting, Neural Networks, vol. 9, Apr. 1996.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature, vol. 323, Oct. 1986.
S. Herculano-Houzel, The Human Brain in Numbers: A Linearly Scaled-up Primate Brain, Front. Hum. Neurosci, vol. 3, Nov. 2009.
Hornick, Multilayer feedforward networks are universal approximators, Neural Networks Volume 2, Issue 5, 1989, Pages 359-366.
Vapnik, The Nature of Statistical Learning Theory, Book, 2013.
Sutskever, I., Martens, J., Dahl, G., Hinton, G., On the importance of initialization and momentum in deep learning, 30th International Conference on Machine Learning, ICML 2013.

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Deep Learning with TensorFlow 2 and Keras - Second Edition

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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.

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