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

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)

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


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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
English
language

Content

Introduction

What are GANs ? Generative Adversarial Networks (GANs)
Import TensorFlow and other libraries
Load and prepare the dataset
Create the models – The Generator
Create the models – The Discriminator
Define the loss and optimizers
Define the training loop
Train the model – Part
Create a GIF
GAN vs DCGAN difference
Source code – for the course
Download the Source code
Output

Extra Reading

Generative Adversarial Networks
Deep Convolutional Generative Adversarial Network – Research paper

Revision – Neural Networks

Setting up the Environment : Anaconda
KERAS Tutorial : Developing an Artificial Neural Network in Python [Step by Step