Enhancing Multi-Layer Perceptron Performance: Demystifying Optimizers

Choosing the Right Optimizer for MLPs

Introduction

Optimizers are algorithms or methods used to adjust the attributes of a model, such as its weights and learning rate, in order to minimize the error or loss function during the process of training a machine learning model. The main objective of an optimizer is to find the optimal set of parameters that result in the best performance of the model on the given dataset.

Gradient Descent …

algorithms deep learning error function layer loss machine machine learning machine learning model multi layer perceptron optimization perceptron performance process rate stochastic-gradient training