The post CSIR UGC NET/NORM/INNER PRODUCT SPACE/LINEAR ALGEBRA/CONCEPT OF MATHEMATICS appeared first on JJtheTutor.

]]>This video teach you what is norm or length of a vector in any inner product space.

Also this video will teach you what is distance between two vector in inner product space with complete concept of mathematics and from zero to infinity level with solved examples.

In this video you will also find relation between norm and magnitude and when will be the distance between two vectors converts into distance between two points.

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]]>The post TIFR GS 2020 LINEAR ALGEBRA MATHEMATICS SOLUTION PART A Qn. 17 (CSIR UGC JRF/NET GATE NBHM IIT JAM) appeared first on JJtheTutor.

]]>TIFR 2020 GS MATHEMATICS PART A Qn. 17 LINEAR ALGEBRA DIAGONALIZABILITY OF MATRICES|| For other videos please browse || https://www.youtube.com/channel/UCKc7gRVNzbRwv_ISBaqSXRA/videos

https://www.youtube.com/watch?v=R2MEiwBJTYM

This video contains the complete explained solution of TIFR 2020 GS mathematics Qn. 17 of Part A.

Subscribe for further videos

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]]>The post Linear Algebra – What are eigenvalues and eigenvectors intro appeared first on JJtheTutor.

]]>Mathematics for Machine Learning: Linear Algebra, Module 5 Eigenvalues and Eigenvectors Application to Data Problems

To get certificate subscribe at: https://www.coursera.org/learn/linear-algebra-machine-learning/home/welcome

============================

Mathematics for Machine Learning: Linear Algebra:

https://scsa.ge/en/online-courses/

https://www.facebook.com/cyberassociation/

About this course: In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets – like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we’re aiming at data-driven applications, we’ll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you’ll write code blocks and encounter Jupyter notebooks in Python, but don’t worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

Who is this class for: This course is for people who want to refresh their maths skills in linear algebra, particularly for the purposes of doing data science and machine learning, or learning about data science and machine learning. We look at vectors, matrices and how to apply these to solve linear systems of equations, and how to apply these to computational problems.

Created by: Imperial College London

Module 5 Eigenvalues and Eigenvectors Application to Data Problems

Eigenvectors are particular vectors that are unrotated by a transformation matrix, and eigenvalues are the amount by which the eigenvectors are stretched. These special ‘eigen-things’ are very useful in linear algebra and will let us examine Google’s famous PageRank algorithm for presenting web search results. Then we’ll apply this in code, which will wrap up the course.

Learning Objectives

• Identify geometrically what an eigenvector/value is

• Apply mathematical formulation in simple cases

• Build an intuition of larger dimention eigensystems

• Write code to solve a large dimentional eigen problem

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]]>The post Linear Algebra 5.1.1 Eigenvectors and Eigenvalues appeared first on JJtheTutor.

]]>The post Correction Video | LINEAR ALGEBRA – Lecture 24 Timing – 46:00 appeared first on JJtheTutor.

]]>#UPSCMathematics #MathematicsOptional #IMS #CivilServices #Trending #UPSC

Video Credit: Govind Gupta (CEO, The Seven Production)

Channel Link : https://www.youtube.com/channel/UCPIXs0ZKnQ20wmWAKYH21WA

Course Credit: Prabhash Kumar (IIT Kharagpur)

Acknowledgements & References:

- Prof. Seymour Lipschutz (Temple University)
- Prof. Marc Lars Lipson (University of Virginia)
- Srikar Sir (Madeeasy Group)

Whatsapp group UPSC Mathematics Optional

https://chat.whatsapp.com/LbJTTzLgGi8ERl0xfvouqH

CONNECT WITH US ON SOCIAL MEDIA

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]]>The post Linear algebra intro and genreal idea appeared first on JJtheTutor.

]]>Here, you can find the link for linear algebra course of Prof. Sivakumar (IITM-Math department) : https://www.youtube.com/playlist?list=PLbMVogVj5nJQ2vsW_hmyvVfO4GYWaaPp7

please share and subscribe….

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]]>The post Lecture # 02 Linear Algebra appeared first on JJtheTutor.

]]>Linear systems in 3D, Elementary row operations, Echelon and Reduced Echelon forms of matrices, Rank of amtrix

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]]>The post Linear Algebra – The Gram–Schmidt process appeared first on JJtheTutor.

]]>Mathematics for Machine Learning: Linear Algebra, Module 4 Matrices make linear mappings

To get certificate subscribe at: https://www.coursera.org/learn/linear-algebra-machine-learning/home/welcome

============================

Mathematics for Machine Learning: Linear Algebra:

https://scsa.ge/en/online-courses/

https://www.facebook.com/cyberassociation/

About this course: In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets – like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we’re aiming at data-driven applications, we’ll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you’ll write code blocks and encounter Jupyter notebooks in Python, but don’t worry, these will be quite short, focussed on the concepts, and will guide you through if you’ve not coded before. At the end of this course you will have an intuitive understanding of vectors and matrices that will help you bridge the gap into linear algebra problems, and how to apply these concepts to machine learning.

Who is this class for: This course is for people who want to refresh their maths skills in linear algebra, particularly for the purposes of doing data science and machine learning, or learning about data science and machine learning. We look at vectors, matrices and how to apply these to solve linear systems of equations, and how to apply these to computational problems.

Created by: Imperial College London

Module 4 Matrices make linear mappings

In Module 4, we continue our discussion of matrices; first we think about how to code up matrix multiplication and matrix operations using the Einstein Summation Convention, which is a widely used notation in more advanced linear algebra courses. Then, we look at how matrices can transform a description of a vector from one basis (set of axes) to another. This will allow us to, for example, figure out how to apply a reflection to an image and manipulate images. We’ll also look at how to construct a convenient basis vector set in order to do such transformations. Then, we’ll write some code to do these transformations and apply this work computationally.

Learning Objectives

• Identify matrices as operators

• Relate the transformation matrix to a set of new basis vectors

• Formulate code for mappings based on these transformation matrices

• Write code to find an orthonormal basis set computationally

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]]>The post #1(a) Find Linear Transformation Of Vector space in Linear Algebra appeared first on JJtheTutor.

]]>#LinearTransformation#Bsc#LinearAlgebra#Engeneering maths

Part 1 linear transformation

https://youtu.be/bt8B3lCDMsI

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]]>The post Linear Algebra: Intro to Vectors in Linear Algebra appeared first on JJtheTutor.

]]>Thanks for watching!

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