Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models
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| Publisher | Packt Publishing |
| ISBN 13 | 9781800200883 |
| ISBN 10 | 1800200889 |
| Author | Joseph Babcock |
| Language | English |
| Book Description | 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 alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook 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 yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is 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. |
| About the Author | Joseph Babcock has spent more than a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Through his career he has worked on recommender systems, petabyte scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to the field of drug discovery and genomics.Raghav Bali is an author of multiple well received books and a Senior Data Scientist at one of the world's largest healthcare organizations. His work involves research and development of enterprise-level solutions based on Machine Learning, Deep Learning, and Natural Language Processing for Healthcare and Insurance-related use cases. His previous experiences include working at Intel and American Express. Raghav has a master's degree (gold medalist) from the International Institute of Information Technology, Bangalore. |
| Publication Date | 2021-04-30 |
| Number of Pages | 488 pages |
Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models
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