Insightforge | AI & Data Science on Instagram: "Double descent describes a surprising pattern where a model’s test error drops, then rises, and then drops again as the model becomes more complex. Traditionally, as complexity increases, a model reduces bias at first but eventually begins to overfit, causing the test error to rise. Modern research has shown that after this overfitting spike, the test error can actually fall again, creating the double-descent curve. A simple example is fitting poly

Orthogonality of Zernike modes in phase profiles estimated using Zonal wavefront sensor and transport of intensity phase retrieval method

Top 4 Text Visualization Examples