This article provides an overview of gradient descent optimization algorithms, focusing on their behavior and practical applications. It begins by introducing the three variants of gradient descent: batch, stochastic, and mini-batch gradient descent, highlighting their trade-offs between accuracy and computational efficiency. The challenges associated with vanilla mini-batch gradient descent, such as choosing the right learning rate and avoiding saddle points, are discussed. The article then delves into various optimization algorithms designed to address these challenges, including Momentum, Nesterov Accelerated Gradient (NAG), Adagrad, Adadelta, RMSprop, Adam, AdaMax, and Nadam. Each algorithm is explained with its update rules and motivations, emphasizing their advantages and disadvantages. For example, Momentum helps in navigating ravines and reducing oscillations, while NAG provides a more anticipatory update by looking ahead at the future position of parameters. The section on parallelizing and distributing SGD covers methods like Hogwild!, Downpour SGD, and Elastic Averaging SGD (EASGD), which aim to speed up training on large datasets by leveraging multiple processors or asynchronous updates. Additional strategies for optimizing SGD, such as shuffling and curriculum learning, batch normalization, early stopping, and adding gradient noise, are also discussed. Finally, the article concludes by summarizing the key points and providing recommendations for choosing the appropriate optimization algorithm based on the specific characteristics of the dataset and model.This article provides an overview of gradient descent optimization algorithms, focusing on their behavior and practical applications. It begins by introducing the three variants of gradient descent: batch, stochastic, and mini-batch gradient descent, highlighting their trade-offs between accuracy and computational efficiency. The challenges associated with vanilla mini-batch gradient descent, such as choosing the right learning rate and avoiding saddle points, are discussed. The article then delves into various optimization algorithms designed to address these challenges, including Momentum, Nesterov Accelerated Gradient (NAG), Adagrad, Adadelta, RMSprop, Adam, AdaMax, and Nadam. Each algorithm is explained with its update rules and motivations, emphasizing their advantages and disadvantages. For example, Momentum helps in navigating ravines and reducing oscillations, while NAG provides a more anticipatory update by looking ahead at the future position of parameters. The section on parallelizing and distributing SGD covers methods like Hogwild!, Downpour SGD, and Elastic Averaging SGD (EASGD), which aim to speed up training on large datasets by leveraging multiple processors or asynchronous updates. Additional strategies for optimizing SGD, such as shuffling and curriculum learning, batch normalization, early stopping, and adding gradient noise, are also discussed. Finally, the article concludes by summarizing the key points and providing recommendations for choosing the appropriate optimization algorithm based on the specific characteristics of the dataset and model.