Virtually Passed
2.0 A better way to understand Differential Equations | Nonlinear Dynamics | 2D Linear Diff Eqns
These second-order linear differential equations can be written in the form dx/dt = ax + by dy/dt = cx + dy Depending on the values of a,b,c and d, the dynamics will be very different! They can be characterized by finding the eigenvalues...
Virtually Passed
A better way to understand Differential Equations | Nonlinear Dynamics (Part 2)
These second-order linear differential equations can be written in the form dx/dt = ax + by dy/dt = cx + dy Depending on the values of a,b,c and d, the dynamics will be very different! They can be characterized by finding the eigenvalues...
Zach Star
I made a (free) linear algebra tool - What I use to make my videos
I made a (free) linear algebra tool - What I use to make my videos
Catalyst University
Postulates of Quantum Mechanics: Eigenvalues & Eigenfunctions
Postulates of Quantum Mechanics: Eigenvalues & Eigenfunctions
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Singular Value Decomposition (SVD)
In this video, we will cover Singular Value Decomposition (SVD). This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Versus SVD
In this video, we will cover PCA versus SVD. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA For Small Sample Size Problems(DualPCA)
In this video, we will cover PCA for small sample size problems (DualPCA). This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA Derivation
In this video, we will cover PCA derivation. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to Z".In...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Lagrange Multipliers
In this video, we will cover Lagrange Multipliers. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and Projects) A to...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Mathematical Foundation: Positive Semi Definite Matrix
In this video, we will cover a positive semi definite matrix. This clip is from the chapter "Machine Learning: Feature Engineering and Dimensionality Reduction with Python" of the series "Data Science and Machine Learning (Theory and...
Professor Dave Explains
Finding Eigenvalues and Eigenvectors
Defining eigenvalues and eigenvectors, and outlining how to solve for them given a particular matrix.
3Blue1Brown
Eigenvectors and Eigenvalues | Essence of Linear Algebra, Chapter 10
Find vectors that stay on their spans after a linear transformation. The 14th video in the series of 15 introduces the concept of eigenvectors, vectors that are only scaled during a linear transformation. The presentation illustrates the...
Khan Academy
Khan Academy: Eigen Everything: Introduction to Eigenvalues and Eigenvectors
Video defining eigenvectors as scaled vectors and eigenvalues as their scale value and explaining why they are interesting.
Khan Academy
Khan Academy: Example Solving for the Eigenvalues of a 2 X2 Matrix
A video solving for the eigenvalues of a 2x2 matrix.
Khan Academy
Khan Academy: Eigen Everything: Proof of Formula for Determining Eigenvalues
Proof of formula for determining Eigenvalues.