Generative AI with Python and TensorFlow 2

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 AIbr>/Book Description://Machines are excelling at creative human skills such as painting writing and composing music. Could you be more creative than generative AI?/br>/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./br>/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./br>/Recreate the code that's under the hood and uncover surprising links between text image and music generation./br>/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 imagesbr>/Who 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. /