6 docs tagged with "optimization"

An introduction to derivatives, their definition, rules, and their crucial role in calculating the slope of the loss function, essential for optimization algorithms like Gradient Descent.

Defining the Gradient vector, its mathematical composition from partial derivatives, its geometric meaning as the direction of maximum increase, and its role as the central mechanism for learning in Machine Learning.

Optimizing model performance using GridSearchCV, RandomizedSearchCV, and Halving techniques.

Understanding how models quantify mistakes using MSE, Binary Cross-Entropy, and Categorical Cross-Entropy.

Defining partial derivatives, how they are calculated in multi-variable functions (like the Loss Function), and their role in creating the Gradient vector for optimization.

Understanding the Hessian matrix, second-order derivatives, and how the curvature of the loss surface impacts optimization and model stability.