---
title: "What are some of the most common optimization algorithms used to train deep learning models?"  
description: "What are some of the most common optimization algorithms used to train deep learning models?"  
author: "Utpal Vishwas"  
published: 2023-04-20  
updated: 2023-04-26  
canonical: https://www.mindstick.com/forum/157945/what-are-some-of-the-most-common-optimization-algorithms-used-to-train-deep-learning-models  
category: "artificial intelligence"  
tags: ["ai", "machine learning", "deep learning"]  
reading_time: 1 minute  

---

# What are some of the most common optimization algorithms used to train deep learning models?

What are some of the most [common](https://www.mindstick.com/articles/23170/10-most-common-accounting-mistakes-of-small-business) [optimization](https://yourviews.mindstick.com/view/85459/what-is-conversion-rate-optimization-and-how-to-get-started) [algorithms](https://www.mindstick.com/articles/12297/google-algorithms-why-so-important) used to train [deep](https://yourviews.mindstick.com/view/87451/a-journey-into-the-deep-dubai-s-underwater-wonderland) [learning models](https://www.mindstick.com/forum/160783/what-are-the-main-differences-between-generative-ai-and-traditional-machine-learning-models)?

## Replies

### Reply by Krishnapriya Rajeev

Some of the most common and sophisticated optimization algorithms used to train [deep learning](https://www.mindstick.com/blog/301936/deep-learning-and-its-working) [models](https://www.mindstick.com/news/3071/openai-plans-app-store-for-ai-software-the-information-reports) are:

- **Stochastic Gradient Descent (SGD):** This is the most commonly used optimization algorithm in deep learning. It updates the model's parameters in the direction of the steepest gradient of the loss function.
- **AdaGrad:** This algorithm adapts the learning rate for each parameter based on the historical gradient information, giving smaller learning rates to parameters associated with frequently occurring features.
- **RMSProp:** This algorithm divides the learning rate by a running average of the magnitudes of recent gradients for each parameter, thereby making it less sensitive to noisy gradients.
- **Adam:** This algorithm combines the ideas of momentum and AdaGrad, resulting in an adaptive learning rate that changes according to the momentums of gradients and a running estimate of the second moment of the gradients.


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Original Source: https://www.mindstick.com/forum/157945/what-are-some-of-the-most-common-optimization-algorithms-used-to-train-deep-learning-models

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