Deep Convolutional Generative Adversarial Networks (DCGAN)

Learn to create Generative Adversarial Networks (GAN) & Deep Convolutional Generative Adversarial Networks (DCGAN)

Ratings: 3.38 / 5.00




Description

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


What You Will Learn!

  • Learn the basic principles of Generative Adversarial Networks (GAN)
  • Learn the basic principles of Deep Convolutional Generative Adversarial Networks (DCGAN)
  • Build a Deep Convolutional Generative Adversarial Networks (DCGAN) with step by step guidance
  • Setup the code for Deep Convolutional Generative Adversarial Networks (DCGAN)

Who Should Attend!

  • Anyone who wish to improve the deep learning knowledge
  • students who wish to learn the new trends Deep Convolutional Generative Adversarial Networks (DCGAN)