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Generative AI with Python and TensorFlow 2

Generative AI with Python and TensorFlow 2

By : Joseph Babcock, Raghav Bali
4.4 (27)
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Generative AI with Python and TensorFlow 2

Generative AI with Python and TensorFlow 2

4.4 (27)
By: Joseph Babcock, Raghav Bali

Overview of this book

Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation.
Table of Contents (16 chapters)
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14
Other Books You May Enjoy
15
Index

Summary

In this chapter, we have covered an overview of what TensorFlow is and how it serves as an improvement over earlier frameworks for deep learning research. We also explored setting up an IDE, VSCode, and the foundation of reproducible applications, Docker containers. To orchestrate and deploy Docker containers, we discussed the Kubernetes framework, and how we can scale groups of containers using its API. Finally, I described Kubeflow, a machine learning framework built on Kubernetes which allows us to run end-to-end pipelines, distributed training, and parameter search, and serve trained models. We then set up a Kubeflow deployment using Terraform, an IaaS technology.

Before jumping into specific projects, we will next cover the basics of neural network theory and the TensorFlow and Keras commands that you will need to write basic training jobs on Kubeflow.

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