Optimization Algorithms ~ Future of CIO

Thursday, July 18, 2024

Optimization Algorithms

8:09 AM Pearl Zhu No comments

There are many advanced optimization techniques used to train deep learning models more efficient.

Optimization algorithms are fundamental to the training of machine learning models, including neural networks. These algorithms are responsible for adjusting the parameters (weights and biases) of the model iteratively during the training process to minimize a defined objective function, often referred to as the loss function.

Here are some commonly used optimization algorithms in machine learning:

Gradient Descent: Gradient descent is a first-order optimization algorithm used to minimize the loss function by iteratively updating the model parameters in the direction of the negative gradient of the loss function. There are different variants of gradient descent, including:

Batch Gradient Descent: Computes the gradient of the loss function with respect to the entire training dataset.

Stochastic Gradient Descent (SGD): Computes the gradient of the loss function with respect to a single training example at a time. It is computationally efficient but may exhibit high variance in the parameter updates.

Mini-batch Gradient Descent: Computes the gradient of the loss function with respect to a small subset of the training dataset (mini-batch). It combines the benefits of batch and stochastic gradient descent, offering a balance between computational efficiency and parameter update stability.

Momentum: Momentum is an extension of gradient descent that introduces a momentum term to accelerate learning and overcome oscillations in parameter updates. It accumulates a velocity vector based on past gradients and uses it to update the parameters, allowing the optimizer to maintain directionality and gain momentum when descending along steep gradients.

There are many advanced optimization techniques used to train deep learning models more efficiently. Understanding these technical aspects gives us a solid foundation for delving deeper into the fascinating world of deep learning.