Gradient Descent is the core optimization technique behind modern machine learning and deep learning models. This workshop will guide participants through the intuition, mathematics, and practical implementation of Gradient Descent and its variants.

We will cover key concepts such as cost functions, learning rates, convergence, and common challenges like local minima and overfitting.

Through hands-on exercises, attendees will learn how to apply Gradient Descent to real datasets, visualize the optimization process, and understand how parameter tuning affects model performance.

Whether you're a beginner or already familiar with ML concepts, this session will strengthen your understanding and give you practical skills to optimize your models efficiently.