MIT 18.S096 Matrix Calculus For Machine Learning And Beyond, IAP 2023

Total watch time

13h 55m

at 1× speed · exactly 13 hours, 55 minutes, 12 seconds at 1×

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1×	13h 55m 12s
1.25×	11h 8m 10s
1.5×	9h 16m 48s
1.75×	7h 57m 15s
2×	6h 57m 36s

Average video49m 8s

Longest1h 13m 57s

Shortest28m 3s

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About 14 days at an hour a day

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Videos (17)

13h 55m in total · tick what you've watched

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1	Lecture 1 Part 1: Introduction and Motivation	57:42	2023-10-23
2	Lecture 1 Part 2: Derivatives as Linear Operators	48:28	2023-10-23
3	Lecture 2 Part 1: Derivatives in Higher Dimensions: Jacobians and Matrix Functions	1:13:57	2023-10-23
4	Lecture 2 Part 2: Vectorization of Matrix Functions	30:18	2023-10-23
5	Lecture 3 Part 1: Kronecker Products and Jacobians	53:05	2023-10-23
6	Lecture 3 Part 2: Finite-Difference Approximations	51:10	2023-10-23
7	Lecture 4 Part 1: Gradients and Inner Products in Other Vector Spaces	1:03:49	2023-10-23
8	Lecture 4 Part 2: Nonlinear Root Finding, Optimization, and Adjoint Gradient Methods	44:26	2023-10-23
9	Lecture 5 Part 1: Derivative of Matrix Determinant and Inverse	28:03	2023-12-01
10	Lecture 5 Part 2: Forward Automatic Differentiation via Dual Numbers	36:02	2023-12-01
11	Lecture 5 Part 3: Differentiation on Computational Graphs	32:46	2023-12-01
12	Lecture 6 Part 1: Adjoint Differentiation of ODE Solutions	58:21	2023-10-23
13	Lecture 6 Part 2: Calculus of Variations and Gradients of Functionals	42:32	2023-10-23
14	Lecture 7 Part 1: Derivatives of Random Functions	1:06:18	2023-10-23
15	Lecture 7 Part 2: Second Derivatives, Bilinear Forms, and Hessian Matrices	46:09	2023-10-23
16	Lecture 8 Part 1: Derivatives of Eigenproblems	36:37	2023-10-23
17	Lecture 8 Part 2: Automatic Differentiation on Computational Graphs	1:05:29	2023-10-23

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