Deep Convolutional Generative Adversarial Networks (DCGAN)
- 3.6
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Brief Introduction
Learn to create Generative Adversarial Networks (GAN) & 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
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
Requirements
- Requirements
- Basic neural networks knowledge would be helpful, however all the basic concepts will be covered in this course
Academy of Computing & Artificial Intelligence
3.6 Raiting
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- 0.0
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- 4.6
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