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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

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803232911
Length 698 pages
Edition 3rd Edition
Tools
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Authors (3):
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Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
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Table of Contents (23) Chapters Close

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

References

  1. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1985). Learning Internal Representations by Error Propagation. No. ICS-8506. University of California, San Diego. La Jolla Institute for Cognitive Science: http://www.cs.toronto.edu/~fritz/absps/pdp8.pdf
  2. Hinton, G. E. and Salakhutdinov, R. R. (2016). Reducing the dimensionality of data with neural networks. science 313.5786: 504–507: https://www.cs.toronto.edu/~hinton/science.pdf
  3. Masci, J. et al. (2011). Stacked convolutional auto-encoders for hierarchical feature extraction. Artificial Neural Networks and Machine Learning–ICANN 2011: 52–59: https://www.semanticscholar.org/paper/Reducing-the-dimensionality-of-data-with-neural-Hinton-Salakhutdinov/46eb79e5eec8a4e2b2f5652b66441e8a4c921c3e
  4. Japkowicz, N., Myers, C., and Gluck, M. (1995). A novelty detection approach to classification. IJCAI. Vol: https://www.ijcai.org/Proceedings/95-1/Papers/068.pdf
  5. Sedhain, S. (2015)...
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