✳flâneur — a map of the web's best reading

Linear regression: Gradient descent  |  Machine Learning  |  Google for Developers

developers.google.com · 1,803 words · saved by 1 readers

Gradient descent is a mathematical technique that iteratively finds the weights and bias that produce the model with the lowest loss. Gradient descent finds the best weight and bias by repeating the following process for a number of user-defined iterations. The model begins training with randomized weights and biases near zero, and then repeats the following steps: Calculate the loss with the current weight and bias. Determine the direction to move the weights and bias that reduce loss. Move the weight and bias values a small amount in the direction that reduces loss. Return to step one and repeat the process until the model can't reduce the loss any further. The diagram below outlines the iterative steps gradient descent performs to find the weights and bias that produce the model with the lowest loss. Figure 12. Gradient descent is an iterative process that finds the weights and bias that produce the model with the lowest loss. At a concrete level, we can walk through the grad

Home Products Machine Learning ML Concepts Crash Course Send feedback Linear regression: Gradient descent Stay organized with collections Save and categorize content based on your preferences. Page Summary outlined_flag Gradient descent is an iterative optimization algorithm used to find the best weights and bias for a linear regression model by minimizing the loss function. The process involves repeatedly calculating the loss, determining the direction to adjust weights and bias to reduce loss, and updating the parameters accordingly. A model is considered to have converged when further itera

Explore this link on the map →

saved by

related reading