What is the history of gradient descent?

What is the history of gradient descent?

Gradient descent is an optimization algorithm that has its roots in the 19th century, with early work by mathematicians like Cauchy.

Who developed it further in the 20th century?

In the 20th century, researchers like David Rumelhart and Geoffrey Hinton popularized gradient descent in the context of neural networks.

How has it evolved with modern machine learning?

Gradient descent has evolved with techniques like stochastic gradient descent and adaptive learning rates, improving efficiency in training models.

Are there any notable applications of gradient descent?

Yes, gradient descent is widely used in training deep learning models, image recognition, and natural language processing.