Adam vs SGD vs AdaGrad vs RMSprop vs AdamW – Day 39

Choosing the Best Optimizer for Your Deep Learning Model When training deep learning models, choosing the right optimization algorithm can significantly impact your model’s performance, convergence speed, and generalization ability. Below, we will explore some of the most popular optimization algorithms, their strengths, the reasons they were invented, and the types of problems they are best suited for. 1. Stochastic Gradient Descent (SGD) Why It Was Invented SGD is one of the earliest and most fundamental optimization algorithms used in machine learning and deep learning. It was invented to handle the challenge of minimizing cost functions efficiently, particularly when dealing with large datasets where traditional gradient descent methods would be computationally expensive. Inventor The concept of SGD is rooted in statistical learning, but its application in neural networks is often attributed to Yann LeCun and others in the 1990s. Formula The update rule for SGD is given by: where is the learning rate, is the gradient of the loss function with respect to the model parameters . Strengths and Limitations **Strengths:** SGD is particularly effective in cases where the model is simple, and the dataset is large, making it a robust choice for problems where generalization is important. The simplicity...

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AdaGrad vs RMSProp vs Adam: Why Adam is the Most Popular? – Day 38

A Comprehensive Guide to Optimization Algorithms: AdaGrad, RMSProp, and Adam In the realm of machine learning, selecting the right optimization algorithm can significantly impact the performance and efficiency of your models. Among the various options available, AdaGrad, RMSProp, and Adam are some of the most widely used optimization algorithms. Each of these algorithms has its own strengths and weaknesses. In this article, we’ll explore why AdaGrad ( which we explained fully on day 37 ) might not always be the best choice and how RMSProp & Adam could address some of its shortcomings. AdaGrad: Why It’s Not Always the Best Choice What is AdaGrad? AdaGrad (Adaptive Gradient Algorithm) is one of the first adaptive learning rate methods. It adjusts the learning rate for each parameter individually by scaling it inversely with the sum of the squares of all previous gradients. The Core Idea: The idea behind AdaGrad is to use a different learning rate for each parameter that adapts over time based on the historical gradients. Parameters with large gradients will have their learning rates decreased, while parameters with small gradients will have their learning rates increased. The Core Equation: Where: represents the parameters at time step . is the...

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