What Optimizer Meaning, Applications & Example
Algorithm that adjusts model parameters during training.
What is an Optimizer?
An Optimizer is an algorithm used in machine learning to adjust the model ’s parameters (weights) during training in order to minimize the loss function. By iteratively updating parameters, optimizers help the model converge to an optimal solution.
Types of Optimizers
- Stochastic Gradient Descent (SGD): Updates parameters using the gradient of the loss function with respect to each training example, usually one at a time.
- Momentum: Accelerates SGD by considering the previous parameter update to smooth out the learning process.
- Adam (Adaptive Moment Estimation): Combines the benefits of Momentum and RMSProp to adapt the learning rate for each parameter individually.
Applications of Optimizers
- Deep Learning : Used in training deep neural networks to improve performance by finding optimal weights.
- Computer Vision : Helps optimize models for image classification and object detection .
- Natural Language Processing: Fine-tunes language models to improve text generation and understanding tasks.
Example of Optimizer
In training a neural network for image classification, the Adam optimizer can be used to adjust the weights of the network, improving its ability to accurately classify images by minimizing the classification error over time.