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024	8	0	_a9781800208506
040			_aPACKT _cPACKT
041			_aen
044			_aGB
100	0		_aJoseph Babcock _eauthor.
700	0		_aRaghav Bali _eauthor.
710	2		_aPACKT
773	0		_tGenerative AI with Python and TensorFlow 2 _dGB,Packt,2021-04-30 _h488
245	0	0	_aGenerative AI with Python and TensorFlow 2.
300			_a488.
377			_aen
260			_aGB: _bPackt, _c2021-04-30.
263			_a2021-04-30
264		1	_aGB: _bPackt,
520			_a<p><b>This edition is heavily outdated and we have a new edition with PyTorch examples published!</b></p><h4>Key Features</h4><ul><li>Code examples are in TensorFlow 2, which make it easy for PyTorch users to follow along</li><li>Look inside the most famous deep generative models, from GPT to MuseGAN</li><li>Learn to build and adapt your own models in TensorFlow 2.x</li><li>Explore exciting, cutting-edge use cases for deep generative AI</li></ul><h4>Book Description</h4>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.<h4>What you will learn</h4><ul><li>Export the code from GitHub into Google Colab to see how everything works for yourself</li><li>Compose music using LSTM models, simple GANs, and MuseGAN</li><li>Create deepfakes using facial landmarks, autoencoders, and pix2pix GAN</li><li>Learn how attention and transformers have changed NLP</li><li>Build several text generation pipelines based on LSTMs, BERT, and GPT-2</li><li>Implement paired and unpaired style transfer with networks like StyleGAN</li><li>Discover emerging applications of generative AI like folding proteins and creating videos from images</li></ul><h4>Who this book is for</h4>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.
538			_aData in extended ASCII character set.
538			_aMode of access: Internet.
856	4	0	_uhttps://learning.packt.com/product/425354
999			_c15002 _d15002