360 Tech Engineering

Mar 27, 2019 · Artificial Intelligence

Understanding Gradient Descent: Basics, Advantages, and Limitations

This article explains the fundamental principle of gradient descent as the steepest‑descent optimization method, derives its direction using Taylor expansion and the Cauchy‑Schwarz inequality, illustrates why it can be slow on functions like Rosenbrock, and discusses its advantages and convergence properties.