PR Description

The chat discusses various optimization algorithms used in machine learning, particularly focusing on gradient-based methods. Each optimization algorithm is explained with its mathematical formulation, intuition, advantages, disadvantages, and a corresponding Python implementation. These algorithms include Gradient Descent, Stochastic Gradient Descent (SGD), Mini-Batch Gradient Descent, Momentum, Nesterov Accelerated Gradient (NAG), AdaGrad, RMSprop, and Adam. The discussion provides a comprehensive understanding of optimization techniques, their practical implementations, and considerations for choosing the appropriate algorithm based on different scenarios and problem domains.

Related Issues

• Closes Addition of Optimizers in ML #527

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