Главная
Study mode:
on
1
Introduction
2
Playing with transformations
3
Why Image Processing to learn Julia?
4
Last lecture leftovers: Perspective maps, Linear perspective interactive
5
Julia style(advanced): Defining vector valued functions
6
Functions with parameters
7
Linear transformations: a collection
8
Nonlinear transformations: a collection
9
Composition
10
Difference between sin and sin(x)
11
Definition of Linear Transformations
12
To be discussed in next lecture
Description:
Explore transformations, composability, and linearity in this MIT Computational Thinking Spring 2021 lecture. Dive into image processing as a tool for learning Julia programming, examine perspective maps and linear perspective interactively, and master advanced Julia techniques for defining vector-valued functions. Discover various linear and nonlinear transformations, understand function composition, and grasp the distinction between sin and sin(x). Conclude with a comprehensive definition of linear transformations, setting the stage for future discussions.

Transformations - Composability and Linearity in Computational Thinking - Lecture 4

The Julia Programming Language
Add to list
0:00 / 0:00