Intro to optimization in deep learning: Momentum, RMSProp and Adam

Introduction

Optimization algorithms are very important while training any deep learning models by adjusting the model’s parameters to minimize the loss function. The most basic method, Stochastic Gradient Descent (SGD), is widely used, but advanced techniques like Momentum, RMSProp, and Adam improve convergence speed and stability. These optimizers build upon SGD by adding mechanisms like adaptive learning rates and momentum, making them more effective for complex models and large datasets.