Gradient Descent

- Overview

Gradient descent is an algorithm used in machine learning (ML) and neural networks to train models by minimizing the difference between predicted and actual results. It's an iterative process that works by gradually reducing the steepness of a curve until it reaches its lowest point, or point of convergence. 

Training data helps these models learn over time, and the cost function in gradient descent acts as a barometer, measuring its accuracy with each iteration of parameter updates. Until the function approaches or equals zero, the model will continue to adjust its parameters to produce the smallest possible error. Once ML models are optimized for accuracy, they can become powerful tools for AI and computer science applications.

Here are some things to know about gradient descent:

• How it works: Gradient descent uses a cost function to optimize parameters and show the accuracy of a model. It's typically run first with training data, and errors on the predictions are used to update the model's parameters.
• When it's used: Gradient descent is most appropriate when linear calculation can't reach an accurate conclusion, or when an optimization algorithm is needed to search for the target.
• Benefits: Gradient descent is scalable, efficient, flexible, and simple. It can efficiently explore the impact of small adjustments to feature weights when training on large data sets.
• History: Gradient descent was invented in 1847 by mathematician Augustin-Louis Cauchy to solve astronomy calculations.

Gradient descent is an optimization algorithm which is commonly-used to train ML models and neural networks. It trains ML models by minimizing errors between predicted and actual results.

Gradient descent is a popular optimization strategy that is used when training data models, can be combined with every algorithm and is easy to understand and implement. Everyone working with machine learning should understand its concept.