Tutorial on Optimization for Machine Learning
stanford.edu
Tutorial on Optimization for Machine Learning
This tutorial provides an introduction to the theory and practice of optimization for machine learning. Starting from the basics, Navid Azizan, postdoctoral scholar, Autonomous Systems Lab, will cover the fundamentals of convex optimization and iterative optimization algorithms, such as gradient descent (GD) and its popular variant, stochastic ...
2021年8月4日
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AI | Machine Learning | Tech on Instagram: "Stochastic Gradient Descent (SGD) is an optimization method used to minimize a function, usually a loss function, to improve a model. Think of it like trying to get to the lowest point of a hill: the gradient represents the slope, telling you which direction to move to go down. Instead of looking at the entire hill (or dataset), SGD picks small random subsets of data at each step, calculating the gradient based on that subset. This makes updates faster
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