000			03117cam a2200289zu 4500
001			88914006
003			FRCYB88914006
005			20251020123917.0
006			m o d
007			cr un
008			251020s2021 fr | o|||||0|0|||eng d
020			_a9781800200883
035			_aFRCYB88914006
040			_aFR-PaCSA _ben _c _erda
100	1		_aBabcock, Joseph
245	0	1	_aGenerative AI with Python and TensorFlow 2 _c['Babcock, Joseph', 'Bali, Raghav']
264		1	_bPackt Publishing _c2021
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aBabcock, Joseph
700	0		_aBali, Raghav
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88914006 _qtext/html _a
520			_aUnderstand the theory behind deep generative models and experiment with practical examplesKey FeaturesBuild a solid understanding of the inner workings of generative modelsExperiment with practical TensorFlow 2.x implementations of state-of-the-art modelsExplore a wide range of current and emerging use cases for deep generative AIBook DescriptionDeep generative models are powerful tools that rival human creative capabilities. In this book, you'll discover how these models emerged, from restricted Boltzmann machines and deep belief networks to VAEs, GANs, and beyond. You'll develop a foundational understanding of generative AI and learn how to implement models yourself in TensorFlow, supported by references to seminal and current research. After getting to grips with the fundamentals of deep neural networks, you'll set up a scalable code lab in the cloud and begin to explore the huge breadth of potential use cases for generative models. You'll look at Open AI's news generator, networks for style transfer and deepfakes, synergy with reinforcement learning, and more. As you progress, you'll recreate the code that makes these possible, piecing together TensorFlow layers, utility functions, and training loops to uncover links between the different modes of generation. By the end of this book, you will have acquired the knowledge to create and implement your own generative AI models.What you will learnImplement paired and unpaired style transfer with networks like StyleGANUse facial landmarks, autoencoders, and pix2pix GAN to create deepfakesBuild several text generation pipelines based on LSTMs, BERT, and GPT-2, learning how attention and transformers changed the NLP landscapeCompose music using hands-on LSTM models, simple GANs, and the intricate MuseGANTrain a deep learning agent to move through a simulated physical environmentDiscover emerging applications of generative AI, such as folding proteins and creating videos from images Who this book is forThis book will appeal to Python programmers, seasoned modelers, and machine learning engineers who are keen to learn about the creation and implementation of generative models. To make the most out of this book, you should have a basic familiarity with probability theory, linear algebra, and deep learning.
999			_c1555777 _d1555777