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

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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.

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

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Paperback book shipped to your preferred address

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Frequently bought together

Generative AI with Python and TensorFlow 2

AU$90.99

Machine Learning with PyTorch and Scikit-Learn

AU$75.99

Machine Learning for Time-Series with Python

AU$75.99

Total AU$ 242.97

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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!

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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.

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Ivan and Zorro Jun 16, 2021

New and fascinating take on AI/machine learning where your models can generate new unstructured data. Want to see the Mona Lisa as if it were painted by Picasso or Van Gogh? That’s generative AI. It’s the same technology that Google was afraid to release to the public for fear of an overwhelming torrent of really fake news. This book assumes you have a good handle on machine learning concepts, but does start with a review of Bayes. It then jumps right into Tensorflow and companion tools for development such as Docker, AWS and Kubeflow. Definitely a long way from toy examples in a Jupyter notebook. If you are interested in going beyond prediction into creation this is a good place to start.

Claudio Rodriguez May 16, 2021

A few years ago, I was fascinated by Generative AI while reading Deep Learning with Python by Francois Chollet. The book was not about generative AI, and only 1 chapter was devoted to a high-level view and some great examples.Generative AI with Python and TensorFlow 2 goes into a deeper dive with Generative AI, starting with controlling a fleet of servers to a fast crash course in Deep Neural Networks. By Chapter 4, you're already generating data, which is mostly text. The explanations of each subject are fantastic, and I'm deeply grateful for all the graphics the authors provided to explain. I don't work with matrices daily, so I find the formulas very useful.I'm still going through the book, but there was one particular reason I was interested in it. Chapter 11 is about Music Generation, which is another part I find fascinating. While it's mostly an explanation of different models to analyze music, I find that I was looking for just that. - If you're looking to get started with Generating things with Generative AI, this is a great place to start. - If you're a seasoned AI generator, the main advantage of this book is using Tensorflow 2 for its examples, but you probably already saw this material from other places.

SoImpossible Aug 22, 2021

This book provides thorough insight not only with deep neural networks, GANs and emerging applications in generative AI, but also covers some very important theory with mathematical derivations. What really stood out to me, was the containerisation using Docker and Kubernetes which is vital for developing and deploying your AI projects. Example configurations are provided within the book itself, with detailed explanations and an overview of what those mean. I have only scratched the surface with this text, but I am looking forward to thoroughly studying the techniques for composing music with generative models. I am also very excited about the emerging applications such as discovering pharmaceutical drugs using generative models. Generative AI for medical applications is going to be huge and this book is a great way to become familiar with this exciting new field.

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

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