Raghav Bali

Joseph Babcock

Packed with intriguing real-world projects as well as theory, Generative AI with Python and TensorFlow 2 enables you to leverage artificial intelligence creatively and generate human-like data in the form of speech, text, images, and music.

Harness the Power of Generative Models to Create Images, Text, and Music

Generative AI with Python and TensorFlow 2

Fun and exciting projects to learn what artificial minds can create Key FeaturesCode 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 learnExport 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.

Joseph Babcock

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