Transformations & AutoDiff | Lecture 3 | MIT Computational Thinking Spring 2021

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Contents
00:00 Introduction by MIT's Prof. Alan Edelman
00:35 Agenda of lecture
01:30 Transformations and automatic differentiation
02:30 General Linear Transformation
02:46 Shear Transformation
04:48 Non-Linear Transformation (Warp)
06:12 Rotation
06:25 Compose Transformation(Rotate followed by Warp)
07:28 More Transformations(xy, rθ)
08:01 Linear and Non-Linear Transformations
09:15 Linear combinations of Images
09:45 Functions in Maths and in Julia (short form, anonymous and long form)
14:52 Automatic Differentiation of Univariates
21:16 Scalar Valued Multivariate Functions
27:10 Automatic Differentiation: Scalar valued and Multivariate Functions
30:20 Minimizing "loss function" in Machine Learning
31:35 Transformations: Vector Valued Multivariate Functions
39:42 Automatic Differentiation of Transformations
14:24 But what is a transformation, really?
49:50 Significance of Determinants in Scaling
53:03 Resource for Automatic Differentiation in 10 minutes with Julia

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