Learn how to create custom images using Generative Adversarial Networks (GANs) and Stable Diffusion models with just a few lines of Python code. This course covers the fundamentals of GANs, neural networks, deep convolutional GANs, TensorFlow Keras, transposed convolution, training loops, generator loss, and code walkthroughs. The teaching method includes theoretical explanations, practical coding examples, and performance result discussions. This course is intended for individuals interested in machine learning, artificial intelligence, and image generation.
Overview
Syllabus
Introduction
What are GANs
Generator vs Discriminator
Neural Networks
Deep convolutional Gans
Convolutional computation
Tensorflow Keras
Transposed Convolution
Training Loop
Generator Loss
Code Walkthrough
Stable Diffusion
Stable Diffusion with KaraCV
Performance Results
Code
Code Example
Questions
Resources
Taught by
TensorFlow
