The Adam Optimizer is a concept in the field of machine learning, particularly deep learning, and it’s used to adjust the way a model learns from data. Let’s break it down in simple terms.
Imagine that you’re trying to find your way in a dark, unfamiliar room. Each step you take is a guess, and you adjust your future steps based on whether your previous guesses got you closer or further from the light switch. You can remember how successful your previous steps were and make better guesses each time.
The Adam Optimizer is similar to this process. When a machine learning model is learning, it makes a lot of “guesses” to understand patterns in the data. Each “guess” it makes, it gets feedback – was the guess right or wrong? The Adam Optimizer helps the model remember its past guesses and how successful they were, so it can make better, more accurate guesses in the future.
In technical terms, Adam Optimizer is an algorithm for gradient descent optimization, which can be used for training deep learning models. It combines the advantages of two other extensions of gradient descent – AdaGrad and RMSProp. It is known for its efficiency and low memory requirement.
« Back to Glossary Index