🌟 Normalizing Flows Tutorial 🌟

Have you ever wondered how we can model complex probability distributions in machine learning? 🤔 Enter Normalizing Flows! This tutorial will guide you through the fascinating world of normalizing flows, a powerful technique for transforming simple distributions into intricate ones.

First, let’s break it down. Normalizing flows use a series of invertible transformations to warp a base distribution (like a Gaussian) into a more complex target distribution. Think of it as bending and stretching clay into any shape you desire. ✨

The key lies in ensuring that the transformation is both invertible and easy to compute. This allows us to calculate probabilities accurately using the change-of-variables formula. 💡

In this tutorial, we’ll explore popular flow architectures like RealNVP and Glow. You’ll learn how these models work step-by-step, complete with code snippets and practical examples. By the end, you'll be equipped to apply normalizing flows to tasks like density estimation and generative modeling. 🚀

Ready to dive in? Let’s make some magic happen! 🔮