Interactive Loss Landscape Visualizer for Neural Network Training

Added Jun 2025 3 design docs

Students meeting gradient descent for the first time are usually shown a single loss curve and asked to trust that optimization is happening somewhere underneath. Actually seeing the surface a network descends — its valleys, plateaus, and traps — makes concepts like local minima and learning rate behavior click in a way equations rarely do. The intern builds an interactive visualization tool in Python that constructs the loss landscape of a small neural network and renders it with Matplotlib as contour and surface plots. Working in Jupyter, users adjust weights by hand and watch their position on the surface move, then launch gradient descent with different learning rates to see convergence, overshooting, and getting stuck in local minima play out visually. The tool contrasts the regions around local and global minima so learners develop intuition for why initialization and step size matter so much in deep learning. The project deepens the intern's own understanding of optimization while exercising scientific Python skills — numerical computation, Matplotlib visualization, and notebook-driven experiment design — and produces a teaching asset that is genuinely useful to other learners studying how neural networks train.

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