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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

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781838823412
Length 646 pages
Edition 2nd Edition
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Authors (3):
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Dr. Amita Kapoor Dr. Amita Kapoor
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Dr. Amita Kapoor
Sujit Pal Sujit Pal
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Sujit Pal
Antonio Gulli Antonio Gulli
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Antonio Gulli
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Table of Contents (19) Chapters Close

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

Jupyter Notebooks on cloud

During development and testing of the model, many in the machine learning community find using Jupyter Notebooks handy; they provide an integrated environment to run and view the result. They are very useful when you are collaborating or want to discuss code with a client. With LaTeX support, many researchers are even shifting to present their research papers on Jupyter, and hence it makes sense to have Jupyter Notebook environment on cloud.

You just share the link and the other person can view it and run it, without any of the hassle of OS environment and software dependencies. In this section we will cover the Jupyter Notebook environments made available by three of the technological giants: Google, Microsoft, and Amazon.

SageMaker

Amazon SageMaker is a fully managed machine learning service. You can use it easily and quickly build and train machine learning models. The trained models can then be directly deployed into a production-ready hosted...

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