18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018, MIT OCW). Instructor: Prof. Gilbert Strang. Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization-and above all a full explanation of deep learning. (from ocw.mit.edu)
Lecture 27 - Backpropagation: Find Partial Derivatives |
In this lecture, Professor Strang presents Professor Sra's theorem which proves the convergence of stochastic gradient descent (SGD). He then reviews backpropagation, a method to compute derivatives quickly, using the chain rule.
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