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

Joseph Babcock

Raghav Bali

zł59.99 ~~zł141.99~~

4.4 (28 Ratings)

eBook Apr 2021 488 pages 1st Edition

Joseph Babcock

Raghav Bali

zł59.99 ~~zł141.99~~

4.4 (28 Ratings)

eBook Apr 2021 488 pages 1st Edition

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Key benefits

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

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.

Who is this book for?

This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.

What you will learn

Export the code from GitHub into Google Colab to see how everything works for yourself
Compose music using LSTM models, simple GANs, and MuseGAN
Create deepfakes using facial landmarks, autoencoders, and pix2pix GAN
Learn how attention and transformers have changed NLP
Build several text generation pipelines based on LSTMs, BERT, and GPT-2
Implement paired and unpaired style transfer with networks like StyleGAN
Discover emerging applications of generative AI like folding proteins and creating videos from images

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Khawaja Muddassar Oct 01, 2024

I would rate my Packt product a solid 5 out of 5! The content is well-structured, informative, and packed with practical examples that enhance understanding and implementation. Highly recommended!

Feefo Verified review

Junling Hu May 25, 2021

This is an excellent book that provides a practical introduction to GAN, VAE and other generative models. It did an excellent job of introducing to Restricted Boltzmann machine (RBM), Variational AutoEncoder (VAE), GAN, LSTM and music generation. It also introduced transformer and GPT, but not in depth. There are coding examples for every chapter, thus making this book very practical to use.It is an ambitious book. It tries to cover many different domains of AI, starting with introduction to neural networks, ending with deep reinforcement learning. The weaker part is in reinforcement learning, which is not necessarily needed for this book.In general, I recommend this book to anyone who is interested in learning GAN and VAE, the two most important generative models in AI today.

Amazon Verified review

dan resnic Jun 14, 2021

My AI experience has always focused on analytics but this provided a really interesting perspective into a subject i knew nothing about.I got the book more out of curiosity and I'll probably never use the examples and teachings, but boy was it fun to read.Do you know how the electronics involved in Pink Floyd's riggs work? Probably not, but if you were to read about it you would probably enjoy every moment of it. The same logic applies here.Highly recommended!

Colbert Philippe Aug 08, 2022

I love this book along several books from PACKT Publishing. This book gives the theory and references for further reading on the top. Excellent work from the Author!

dr t Jul 13, 2021

The book is targeted at programmers and machine learning practitioners who want to learn about generative models. It is not for complete beginners - as the authors themselves state, one should have a basic understanding of probability, linear algebra, and deep learning.The book starts by introducing core concepts such as Docker, Kubernetes and Tensorflow, and then moves onto more advanced (and interesting) topics such as generating digits, images, music and deep fakes, and even copying artist’s styles to generate new art. Consequently, as the reader progresses through the book there are ample opportunities to use his/her coding skills to experiment.For this reader there were two chapters of special interest.- Chapter 8 on using transformers to generate text, in which BERT and GPT are introduced and explained, and then a program created to generate fake headlines. Having recently read Rothman’s book on transformers this chapter was very useful.- Chapter 10 in which various aspects of reinforcement learning are discussed.By the end of the book, this reader definitely felt that he had acquired a much firmer understanding of GANs and what they are capable of. Recommended and a good book to gain knowledge to have in one’s arsenal.

Tool	Project site	Use
Docker	https://www.docker.com/	Application runtime dependency encapsulation
Anaconda	https://www.anaconda.com/	Python language package management
Jupyter	https://jupyter.org/	Interactive Python runtime and plotting / data exploration tool
Kubernetes	https://kubernetes.io/	Docker container orchestration and resource management
Kubeflow	https://www.kubeflow.org/	Machine learning workflow engine developed on Kubernetes
Terraform	https://www.terraform.io/	Infrastructure scripting language for configurable and consistent deployments of Kubeflow and Kubernetes
VSCode	https://code.visualstudio.com/	Integrated development environment (IDE)

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

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