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Automatic Differentiation of Univariates
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Classroom Contents
Transformations and Automatic Differentiation in Computational Thinking - Lecture 3
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- 1 Introduction by MIT's Prof. Alan Edelman
- 2 Agenda of Lecture0-1:30 Transformations and Automatic Differentiations
- 3 General Linear Transformation
- 4 Shear Transformation
- 5 Non-Linear Transformation(Warp)
- 6 Rotation
- 7 Compose Transformation(Rotate followed by Warp)
- 8 More Transformations(xy, rθ)
- 9 Linear and Non-Linear Transformations
- 10 Linear combinations of Images
- 11 Functions in Maths and in Julia(Short form, anonymous and long form)
- 12 Automatic Differentiation of Univariates
- 13 Scalar Valued Multivariate Functions
- 14 Automatic Differentiation: Scalar valued and Multivariate Functions
- 15 Minimizing "loss function" in Machine Learning
- 16 Transformations: Vector Valued Multivariate Functions
- 17 Automatic Differentiation of Transformations
- 18 But what is a transformation, really?
- 19 Significance of Determinants in Scaling
- 20 Resource for Automatic Differentiation in 10 minutes with Julia
