Understanding Gradient Descent Algorithms: Batch, Mini-Batch, and Stochastic

Gradient descent algorithms are fundamental to machine learning optimization, enabling the search for optimal solutions in various problem domains. Among these algorithms, Batch Gradient Descent (BGD), Mini-Batch Gradient Descent (MBGD), and Stochastic Gradient Descent (SGD) are commonly employed. In this blog post, we will explore these algorithms, highlighting their differences and discussing their applications.

Batch Gradient Descent: Batch Gradient Descent (BGD) is the simplest form of gradient descent. It involves computing the gradient of the cost function using the entire training dataset. Here’s an overview of BGD:

• Compute the gradient of the cost function by evaluating it over the entire training dataset.
• Update the model parameters in the opposite direction of the gradient.
• Repeat the process until convergence or a predefined number of iterations.

Advantages of BGD:

• Guarantees convergence to the global minimum for convex cost functions.
• Provides stable convergence and a smooth decrease…