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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
Authors (3):
Amita Kapoor Amita Kapoor
Profile icon Amita Kapoor
Antonio Gulli Antonio Gulli
Profile icon Antonio Gulli
Sujit Pal Sujit Pal
Profile icon Sujit Pal
View More author details

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

H2O.ai

H2O is a fast, scalable machine learning and deep learning framework developed by H2O.ai, released under the open-source Apache license. According to the company website, as of the time of writing this book, more than 20,000 organizations use H2O for their ML/deep learning needs. The company offers many products like H2O AI cloud, H2O Driverless AI, H2O wave, and Sparkling Water. In this section, we will explore its open-source product, H2O.

It works on big data infrastructure on Hadoop, Spark, or Kubernetes clusters and it can also work in standalone mode. It makes use of distributed systems and in-memory computing, which allows it to handle a large amount of data in memory, even with a small cluster of machines. It has an interface for R, Python, Java, Scala, and JavaScript, and even has a built-in web interface.

H2O includes a large number of statistical-based ML algorithms such as generalized linear modeling, Naive Bayes, random forest, gradient boosting, and...

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