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22. Gradient Descent: Downhill to a Minimum

MIT OpenCourseWare 88,001 6 years ago
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MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Gilbert Strang View the complete course: https://ocw.mit.edu/18-065S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k Gradient descent is the most common optimization algorithm in deep learning and machine learning. It only takes into account the first derivative when performing updates on parameters - the stepwise process that moves downhill to reach a local minimum. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu

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