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Deep Learning with TensorFlow 2 and Keras

You're reading from Deep Learning with TensorFlow 2 and Keras Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API

Product type Paperback

Published in Dec 2019

Publisher Packt

ISBN-13 9781838823412

Length 646 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (3):

Dr. Amita Kapoor

Sujit Pal

Antonio Gulli

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 FREE CHAPTER

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

References

Mikolov, T., et al. (2013, Sep 7) Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781v3 [cs.CL].
Mikolov, T., et al. (2013, Sep 17). Exploiting Similarities among Languages for Machine Translation. arXiv:1309.4168v1 [cs.CL].
Mikolov, T., et al. (2013). Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems 26 (NIPS 2013).
Pennington, J., Socher, R., Manning, C. (2014). GloVe: Global Vectors for Word Representation. D14-1162, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Niu, F., et al (2011, 11 Nov). HOGWILD! A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. arXiv:1106.5730v2 [math.OC].
Levy, O., Goldberg, Y. (2014). Neural Word Embedding as Implicit Matrix Factorization. Advances in Neural Information Processing Systems 27 (NIPS 2014).
Mahoney, M. (2011, 1 Sep). text8 dataset. http...

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Authors (3)

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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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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