Gradient Descent: Design Your First Machine Learning Model

Gradient descent is an optimization algorithm that is used to train machine learning models and is now used in a neural network. Training data helps the model learn over time as gradient descent act as an automatic system that tunes parameters to achieve better results. These parameters are updated after each iteration until the function achieves the smallest possible error. The red arrow in the figure below is a gradient and by updating our parameters after each iteration we can reduce loss which is our primary goal. According to Arthur Samuel, gradient descent is the automatic processing of altering weights to maximize performance Fast AI.