Show HN: I created an interactive tool to visualize various ML algorithms (github.com)

A developer has launched an interactive tool designed to visualize the behavior of various Machine Learning algorithms, providing a hands-on experience for users to understand complex concepts. The application showcases how different gradient descent optimizers—such as Vanilla SGD, Momentum, Adagrad, RMSProp, and Adam—navigate intricate loss surfaces in real-time. Utilizing Plotly for stunning 3D visualizations, users can experiment with custom loss surfaces, adjust essential hyperparameters like learning rates and momentum, and manipulate visual animations to deepen their grasp of the algorithms' functionality. This tool is significant for the AI/ML community as it democratizes access to advanced machine learning concepts, making it easier for newcomers and seasoned professionals alike to explore and comprehend important optimization techniques and reinforcement learning algorithms, including Q-Learning (QL), Deep Q-Learning (DQL), and Proximal Policy Optimization (PPO). Through adjustable environments and visualized training steps in scenarios such as maze navigation and the CartPole problem, users can observe agent behaviors and refine their understanding of how these algorithms learn to interact with their environments dynamically.