Generative Adversarial Networks (GANs) & Deep Convolutional Generative Adversarial Networks (DCGAN) are one of the most interesting and trending ideas in computer science today.

Two models are trained simultaneously by an adversarial process. A generator , learns to create images that look real, while a discriminator learns to tell real images apart from fakes.

At the end of the Course you will understand the basics of Python Programming and the basics ofGenerative Adversarial Networks (GANs) & Deep Convolutional Generative Adversarial Networks (DCGAN) .

The course will have step by step guidance

Import TensorFlow and other libraries

Load and prepare the dataset

Create the models (Generator & Discriminator)

Define the loss and optimizers (Generator loss , Discriminator loss)

Define the training loop

Train the model

Analyze the output

Suggested Prerequisites:

• Python coding: some revision is provided during this course

• Gradient descent

• Basic knowledge of neural networks