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Can I read Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models on EtoBox?

Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models by Joseph Babcock, Raghav Bali is a science book available to read on EtoBox.

What is Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models about?

Fun and exciting projects to learn what artificial minds can create Key Features Code examples are in TensorFlow 2, which make it easy for PyTorch users to follow along Look inside the most famous deep generative models, from GPT to MuseGAN Learn to build and adapt your own models in TensorFlow 2.x Explore exciting, cutting-edge use cases for deep generative AI Book Description 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. What you will learn Export the code from GitHub into Google Colab to see how everything w

Who reads Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models?

It is typically read by self-directed learners exploring a subject in depth.

Common subject areas: history, science, philosophy, social sciences.

Author
Joseph Babcock, Raghav Bali
Publisher
Packt Publishing, Limited
Published
2021
Language
EN
ISBN
9781800200883
Category
science
Subjects
Computer Science, Stem
Rating
4.1 / 5 (34 ratings)
Updated
2026-03-14

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