I’m working on an animated series to visualize the math behind Machine Learning (Manim)

Hi everyone :) I have started working on a YouTube series called **"The Hidden Geometry of Intelligence."** It is a collection of animated videos (using Manim) that attempts to visualize the mathematical intuition behind AI, rather than just deriving formulas on a blackboard. **What the series provides:** * **Visual Intuition:** It focuses on the geometry—showing how things like matrices actually warp space, or how a neural network "bends" data to separate classes. * **Concise Format:** Each episode is kept under 3-4 minutes to stay focused on a single core concept. * **Application:** It connects abstract math concepts (Linear Algebra, Calculus) directly to how they affect AI models (debugging, learning rates, loss landscapes). **Who it is for:** It is aimed at developers or students who are comfortable with code (Python/PyTorch) but find the mathematical notation in research papers difficult to parse. It is not intended for Math PhDs looking for rigorous proofs. I just uploaded **Episode 0**, which sets the stage by visualizing how models transform "clouds of points" in high-dimensional space. **Link:**https://www.youtube.com/watch?v=Mu3g5BxXty8 I am currently scripting the next few episodes (covering Vectors and Dot Products). If there are specific math concepts you find hard to visualize, let me know and I will try to include them.