In deep learning, optimizers are the secret heroes that guide models toward better performance. Whether you’re training a small neural network or a massive transformer, selecting the right optimizer is very crucial. This post explores four widely-used optimizers SGD, Momentum, RMSprop, and Adam and how they adapt parameter updates for optimal learning..
Tag: neural networks
Gradient descent is one of the main tools that is used in many machine learning and neural network algorithms. It acts as a guide to finding the minimum of a function. But what’s happening under the hood, and why do we need it?
Categories
Understanding Bias in Neural Networks
Neural networks are systems that mimic certain aspects of human cognition, aiming to identify and learn patterns within complex data. One crucial element that is hard to grasp in these networks is bias. Bias might seem like just another parameter, but it plays a significant role in the learning process.