000			01574 a2200193 4500
005			20250822174848.0
020			_a9781789139907
082			_a006.31 KAL
100			_aKalin, Josh
245			_aGenerative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras
260			_bPackt Publishing _c2018 _aBirmingham
300			_a252
520			_aDeveloping Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use. By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away.
650			_aComputer science
650			_aArtificial Intelligence
650			_aGenerative Adversarial Networks
650			_aCookbook
942			_cBK _2ddc
999			_c51171 _d51171