Creating a Generative Adversarial Network (GAN) using Python and TensorFlow 2.0 can be an exciting project! GANs are known for their ability to generate new data that resembles a given dataset, and TensorFlow is a powerful library for building and training neural networks

This course aims to provide participants with a deep understanding of Generative Artificial Intelligence (Gen AI) using Python and TensorFlow 2.0. Participants will learn the foundations of generative models, explore advanced techniques, and gain practical experience through hands-on projects.

While representing operations in the dataflow graph as primitives allows flexibility in defining new layers within the Python client API, it also can result in a lot of "boilerplate" code and repetitive syntax. For this reason, the high-level API Keras14 was developed to provide a high-level abstraction; layers are represented using Python classes, while a particular runtime environment (such as TensorFlow or Theano) is a "backend" that executes the layer, just as the atomic TensorFlow operators can have different underlying implementations on CPUs, GPUs, or TPUs. While developed as a framework-agnostic library, Keras has been included as part of TensorFlow's main release in version 2.0. For the purposes of readability, we will implement most of our models in this book in Keras, while reverting to the underlying TensorFlow 2.0 code where it is necessary to implement particular operations or highlight the underlying logic. Please see Table 2.3 for a comparison between how various neural network algorithm concepts are implemented at a low (TensorFlow) or high (Keras) level in these libraries.