Generative Adversarial Networks Cookbook
Developing 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.
|Course Length||8 hours 2 minutes|
|Date Of Publication||31 Dec 2018|
|Generative and discriminative models|
|A neural network love story|
|Deep neural networks|
|Architecture structure basics|
|Basic building block – generator|
|Basic building block – discriminator|
|Basic building block – loss functions|
|GAN pieces come together in different ways|
|What does a GAN output?|
|Understanding the benefits of a GAN structure|
|From theory to code – a simple example|
|Building a neural network in Keras and TensorFlow|
|Explaining your first GAN component – discriminator|
|Explaining your second GAN component – generator|
|Putting all the GAN pieces together|
|Training your first GAN|
|Training the model and understanding the GAN output|