Transformations - Composability and Linearity in Computational Thinking - Lecture 4
Offered By: The Julia Programming Language via YouTube
Course Description
Overview
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.
Syllabus
Introduction.
Playing with transformations.
Why Image Processing to learn Julia?.
Last lecture leftovers: Perspective maps, Linear perspective interactive.
Julia style(advanced): Defining vector valued functions.
Functions with parameters.
Linear transformations: a collection.
Nonlinear transformations: a collection.
Composition.
Difference between sin and sin(x).
Definition of Linear Transformations.
To be discussed in next lecture.
Taught by
The Julia Programming Language
Related Courses
Design of Computer ProgramsStanford University via Udacity 人群与网络 People & Networks
Peking University via Coursera Introduction to Computer Science and Programming Using Python
Massachusetts Institute of Technology via edX كيف تفكر الآلات - مقدمة في تقنيات الحوسبة
King Fahd University of Petroleum and Minerals via Rwaq (رواق) Power and elegance of computational thinking
The University of Oklahoma via Janux