Gradient Based Optimization

Gradient based optimization is a mathematical technique used to find the minimum or maximum of a function by iteratively moving in the direction of the negative gradient (for minimization) or positive gradient (for maximization). It is fundamental in machine learning for training models by minimizing loss functions, and in various engineering and scientific fields for solving optimization problems. The method relies on computing derivatives to determine the steepest descent or ascent direction.

Also known as: Gradient Optimization, Gradient Descent, Gradient Ascent, Gradient-Based Methods, Gradient Methods

Why learn Gradient Based Optimization?

Developers should learn gradient based optimization when working with machine learning, deep learning, or any application requiring parameter tuning, such as neural network training, logistic regression, or support vector machines. It is essential for implementing algorithms like gradient descent, stochastic gradient descent (SGD), and Adam, which are used to optimize models by reducing error and improving performance on tasks like image recognition or natural language processing.

Learning Resources

• docs: Stanford CS229 Lecture Notes on Gradient Descent
• video: 3Blue1Brown Video on Gradient Descent
• book: Deep Learning Book Chapter on Optimization
• course: Coursera Course: Machine Learning by Andrew Ng
• tutorial: Towards Data Science Tutorial on Gradient Descent