MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 - 25. Stochastic Gradient Descent

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What you'll learn

This course includes

28 hours of video
Certificate of completion
Access on mobile and TV

Summary

Keywords

stochastic gradient descent SGD Adaptive Moment Estimation ADAM Lasso

Full Transcript

MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Suvrit Sra View the complete course: https://ocw.mit.edu/18-065S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k Professor Suvrit Sra gives this guest lecture on stochastic gradient descent (SGD), which randomly selects a minibatch of data at each step. The SGD is still the primary method for training large-scale machine learning systems. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu