What is Gradient Descent?

Gradient Descent is an iterative mathematical optimization algorithm used to find the minimum value of a function. In the context of computing, it calculates the derivative (or gradient) of a function and incrementally updates variables in the opposite direction of the gradient to reach the lowest possible value.

 

Why is Gradient Descent used in Machine Learning?

It is primarily used to train machine learning models by minimizing the cost function, which measures the error between the model's predictions and the actual data. By minimizing this error, Gradient Descent systematically adjusts the model's internal parameters so that the model produces accurate outputs.

 

What exactly is the "gradient"?

The gradient is a vector that computes the partial derivatives of the model's error function with respect to each of its parameters. It mathematically defines the direction and the exact rate at which the error increases the most. Gradient Descent subtracts this gradient from the current parameters to move in the direction where the error decreases.

 

What is a "Learning Rate" in this context?

The learning rate is a predefined numerical value that controls the size of the adjustments made to the model's parameters during each iteration. If the learning rate is configured too high, the algorithm may overshoot the minimum value. If it is set too low, the algorithm will require a massive amount of computational time to reach the minimum value.

 

What are the standard variations of Gradient Descent?

The three primary types are:

• Batch Gradient Descent: Processes the entire dataset before updating parameters
• Stochastic Gradient Descent: Updates parameters after processing each individual data point
• Mini-batch Gradient Descent: Processes data in fixed-size subsets before updating parameters, offering a balance between computational speed and stability

 

An example of Gradient Descent used in a machine learning model:

In a linear regression algorithm designed to predict housing prices based on square footage, the model starts with randomized weight multipliers. Gradient Descent calculates the mathematical difference between the model's initial price predictions and the actual prices in the dataset. It calculates the gradient of this error and continuously updates the weight multipliers across multiple iterations until the mathematical difference between predicted and actual prices reaches its lowest possible numerical value.