Generative Diffusion Modeling | Explained with Python PyTorch and Easy Examples | Sample AI Project

What are Diffusion Models? In this comprehensive tutorial, dive into the intriguing world of diffusion models in Artificial Intelligence, specifically in deep learning. Learn what diffusion-based deep generative models are, how they work, and the magical process of creating new data samples like images or text through random walks in data space. We'll explore the concept of noising or diffusing data in iterations and training a neural network to denoise it.

What sets this video apart? We're keeping it simple by generating basic data instead of complex images or text. Using Google Colab and PyTorch, we demonstrate how to create a neural network capable of denoising data iteratively to produce new samples resembling the original data. The demonstration uses a unique dataset that ensures specific relationships between the features, and you'll witness how the generated data inherits these relationships.

Whether you're an AI enthusiast, student, or professional, this video provides valuable insights into diffusion models and how to implement them in Python. You can tailor the provided code for your projects, and even experiment with different strategies.

Here is the Google Colab page:

Here is another video on generative AI covering Generative Adversarial Networks (GANs):

Don't miss the chance to explore this powerful approach to generating data. Watch till the end for deleted scenes, and don't forget to like and share! See you soon in another video.

Dr. Shahriar Hossain

#diffusion #generativeai #neuralnetworks #aimodel #modernai