Search results “Inner product in”

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MathTheBeautiful

This lesson discusses the notations involved with the dot product, and the notation that is involved with the inner product. We will go more in depth in the actual book.

Views: 5036
JJtheTutor

Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! Inner Product and Orthogonal Functions , Quick Example.
In this video, I give the definition of the inner product of two functions and what it means for those functions to be orthogonal. I work a quick example showing that two functions are orthogonal.

Views: 131866
patrickJMT

The vector space ν with an inner product is called a (real) inner product space.
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Over 7 years of experience teaching math at 3 universities and a community college. Courses ranged from Intermediate Algebra to Calculus II and class sizes varied from 2 to over 200 students. Tutoring since 2000 formally and informally, individually and in groups, for courses from Geometry to Differential Equations. Please note that I generally will not be available for audio and video in live lessons but my experience has been that audio and video aren't really needed.
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Views: 36654
Chegg

Views: 5495
Jacob Bains

Algebra 1M - international
Course no. 104016
Dr. Aviv Censor
Technion - International school of engineering

Views: 8048
Technion

https://bit.ly/PG_Patreon - Help me make these videos by supporting me on Patreon!
https://lem.ma/LA - Linear Algebra on Lemma
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Views: 1616
MathTheBeautiful

In this lecture, we explore geometric interpretations of vectors in R^n. Specifically, we define the inner product (dot product) of two vectors and the length (norm) of a vector. We also discuss what it means for two vectors in R^n to be orthogonal.

Views: 176
James Hamblin

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https://lem.ma/LA - Linear Algebra on Lemma
https://lem.ma/prep - Complete SAT Math Prep
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Views: 4477
MathTheBeautiful

Definition of an inner and outer product of two column vectors.

Views: 501
Jeffrey Chasnov

Course materials: https://learning-modules.mit.edu/class/index.html?uuid=/course/16/fa17/16.920

Views: 185
Qiqi Wang

Linear Algebra by Dr. K.C. Sivakumar,Department of Mathematics,IIT Madras.For more details on NPTEL visit http://nptel.ac.in

Views: 33166
nptelhrd

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Consider the inner product in the vector space of matrices. R 2x2

Views: 357
JJtheTutor

Algebra 1M - international
Course no. 104016
Dr. Aviv Censor
Technion - International school of engineering

Views: 13301
Technion

Views: 6817
MathTheBeautiful

Section 6.1. Inner Product, Length and Orthogonality

Views: 129
Mihran Papikian

The inner product is an operation that takes two vectors and produces a number. It satisfies several axioms. A vector is normalized if its inner product with itself is 1. Two vectors are orthogonal if their inner product is 0. Normalized, orthogonal vectors are orthonormal.

Views: 1210
daytonellwanger

ME564 Lecture 21
Engineering Mathematics at the University of Washington
Linear algebra in 2D and 3D: inner product, norm of a vector, and cross product
Notes: http://faculty.washington.edu/sbrunton/me564/pdf/L21.pdf
Course Website: http://faculty.washington.edu/sbrunton/me564/
http://faculty.washington.edu/sbrunton/

Views: 1317
Steve Brunton

Views: 8016
MathTheBeautiful

The Elementary Linear Algebra Book : http://amzn.to/2tFxVSY
The Advanced Linear Algebra Book : http://amzn.to/2tyYHON

Views: 3669
ANS ACADEMY

Inner product space in hindi.
Inner product vector space with example.
Solved example of inner product space in hindi.
Inner product space in matrix.
Linear Algebra. Inner product space in hindi.
Gram-Schmidt Orthogonalization Process - Linear Algebra: https://www.youtube.com/playlist?list=PLtFV0hYqGnEmH5UMu8-I8VXKoCwwiwikn
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Mathematics Analysis

It's the first video lesson in a series dedicated to linear algebra (second course). The topics of this video are: Inner Product, Inner Product Space, Euclidean and Unitary Spaces, formal definitions You'll be required to know the basics before taking this course.
If you need supplement the basics, watch the lectures at:
https://www.khanacademy.org/math/linear-algebra
Video by Elitzur Bahir
The videos are based on course number 20229 in the Open University.

Views: 68324
ASTROTZUR

Developed by Dr. Betty Love at the University of Nebraska - Omaha for use in MATH 2050, Applied Linear Algebra.
Based on the book Linear Algebra and Its Applications by Lay.

Views: 7013
Betty Love

Linear Algebra: We define the standard inner product on R^n and explain its basic properties. A cosine formula is given in terms of the inner product and lengths of two vectors.

Views: 24051
MathDoctorBob

Definition of an inner product and some examples

Views: 35420
Gilbert Eyabi

Views: 856
Jacob Bains

Views: 12940
MathTheBeautiful

The properties of inner products on complex vector spaces are a little different from thos on real vector spaces. We go over the modified axioms, look at a few examples, and tackle the complex Schwarz inequality.

Views: 12738
Lorenzo Sadun

Functional Analysis by Prof. P.D. Srivastava, Department of Mathematics, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in

Views: 25027
nptelhrd

Course 3 Mathematics for Machine Learning PCA: Module 2 Inner Products
To get certificate subscribe at: https://www.coursera.org/learn/pca-machine-learning
============================
Mathematics for Machine Learning: Multivariate Calculus https://www.youtube.com/playlist?list=PL2jykFOD1AWa-I7JQfdD-ScBB6XojzmVh
============================
Youtube channel: https://www.youtube.com/user/intrigano
============================
https://scsa.ge/en/online-courses/
https://www.facebook.com/cyberassociation/
About this course: This course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. This examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy
Who is this class for: This is an intermediate level course. It is probably good to brush up your linear algebra and python programming before you start this course.
________________________________________
Created by: Imperial College London
Module 2 Inner Products
Data can be interpreted as vectors. Vectors allow us to talk about geometric concepts, such as lengths, distances and angles to characterise similarity between vectors. This will become important later in the course when we discuss PCA. In this module, we will introduce and practice the concept of an inner product. Inner products allow us to talk about geometric concepts in vector spaces. More specifically, we will start with the dot product (which we may still know from school) as a special case of an inner product, and then move toward a more general concept of an inner product, which play an integral part in some areas of machine learning, such as kernel machines (this includes support vector machines and Gaussian processes). We have a lot of exercises in this module to practice and understand the concept of inner products.
Learning Objectives
• Explain inner products
• Compute angles and distances using inner products
• Write code that computes distances and angles between images
• Demonstrate an understanding of properties of inner products
• Discover that orthogonality depends on the inner product
• Write code that computes basic statistics of datasets

Views: 170
intrigano

Norms and inner product spaces

This video covers the definition of an inner product and an inner product space, length, distance and angles in an inner product space, the inner product on the vector space of continuous functions, orthogonality, the Pythagorean Theorem, and an example from communication engineering of the importance of distance in creating good communication codes.

Views: 196
John Harland

Course 3 Mathematics for Machine Learning PCA: Module 2 Inner Products
To get certificate subscribe at: https://www.coursera.org/learn/pca-machine-learning
============================
Mathematics for Machine Learning: Multivariate Calculus https://www.youtube.com/playlist?list=PL2jykFOD1AWa-I7JQfdD-ScBB6XojzmVh
============================
Youtube channel: https://www.youtube.com/user/intrigano
============================
https://scsa.ge/en/online-courses/
https://www.facebook.com/cyberassociation/
About this course: This course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. This examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy
Who is this class for: This is an intermediate level course. It is probably good to brush up your linear algebra and python programming before you start this course.
________________________________________
Created by: Imperial College London
Module 2 Inner Products
Data can be interpreted as vectors. Vectors allow us to talk about geometric concepts, such as lengths, distances and angles to characterise similarity between vectors. This will become important later in the course when we discuss PCA. In this module, we will introduce and practice the concept of an inner product. Inner products allow us to talk about geometric concepts in vector spaces. More specifically, we will start with the dot product (which we may still know from school) as a special case of an inner product, and then move toward a more general concept of an inner product, which play an integral part in some areas of machine learning, such as kernel machines (this includes support vector machines and Gaussian processes). We have a lot of exercises in this module to practice and understand the concept of inner products.
Learning Objectives
• Explain inner products
• Compute angles and distances using inner products
• Write code that computes distances and angles between images
• Demonstrate an understanding of properties of inner products
• Discover that orthogonality depends on the inner product
• Write code that computes basic statistics of datasets

Views: 175
intrigano

Advanced Engineering Mathematics, Lecture 3.5: Complex inner products and Fourier series.
We begin with a review of how to define the norm in the complex plane, and then how to define the norm in the complex vector space C^n. Just like how the norm in a real vector space can be defined from an inner product, so can the norm in a complex vector space, but there are a few subtle differences. We apply this to the (complex) vector space of periodic functions. Instead of using a basis of sine and cosine waves, we can use a basis of complex exponentials. With respect to the right inner product, these functions form an orthonormal basis. This means that we can write any periodic function uniquely using complex exponentials, called its complex Fourier series. Moreover, just like for real Fourier series, we can derive simple formulas for the Fourier coefficients using the linear algebra theory we've developed -- just take the inner product of f(x) with the corresponding basis function.
Course webpage (with lecture notes, homework, worksheets, etc.): http://www.math.clemson.edu/~macaule/math4340-online.html
Prerequisite: http://www.math.clemson.edu/~macaule/math2080-online.html

Views: 832
Professor Macauley

Linear Algebra 19, Dot product, Inner product

Views: 2237
LadislauFernandes

Advanced Matrix Theory and Linear Algebra for Engineers by Prof. Vittal Rao ,Centre For Electronics Design and Technology, IISC Bangalore. For more details on NPTEL visit http://nptel.iitm.ac.in

Views: 7235
nptelhrd

(This video should be redone, and it might introduce too much new stuff for someone who hasn't already seen it, so it will likely be split into several videos.)
This video introduces the idea that many properties of vector spaces can be extended to functions, and the inner product is used as an example. This is an essential idea in quantum mechanics.

Views: 2353
PhysicsHelps

This course will continue on Patreon at http://bit.ly/PavelPatreon
Textbook: http://bit.ly/ITCYTNew
Solutions: http://bit.ly/ITACMS_Sol_Set_YT Errata: http://bit.ly/ITAErrata
McConnell's classic: http://bit.ly/MCTensors
Weyl's masterpiece: http://bit.ly/SpaceTimeMatter Levi-Civita's classic: http://bit.ly/LCTensors Linear Algebra Videos: http://bit.ly/LAonYT
Table of Contents of http://bit.ly/ITCYTNew
Rules of the Game
Coordinate Systems and the Role of Tensor Calculus
Change of Coordinates
The Tensor Description of Euclidean Spaces
The Tensor Property
Elements of Linear Algebra in Tensor Notation
Covariant Differentiation
Determinants and the Levi-Civita Symbol
The Tensor Description of Embedded Surfaces
The Covariant Surface Derivative
Curvature
Embedded Curves
Integration and Gauss’s Theorem
The Foundations of the Calculus of Moving Surfaces
Extension to Arbitrary Tensors
Applications of the Calculus of Moving Surfaces
Index:
Absolute tensor
Affine coordinates
Arc length
Beltrami operator
Bianchi identities
Binormal of a curve
Cartesian coordinates
Christoffel symbol
Codazzi equation
Contraction theorem
Contravaraint metric tensor
Contravariant basis
Contravariant components
Contravariant metric tensor
Coordinate basis
Covariant basis
Covariant derivative
Metrinilic property
Covariant metric tensor
Covariant tensor
Curl
Curvature normal
Curvature tensor
Cuvature of a curve
Cylindrical axis
Cylindrical coordinates
Delta systems
Differentiation of vector fields
Directional derivative
Dirichlet boundary condition
Divergence
Divergence theorem
Dummy index
Einstein summation convention
Einstein tensor
Equation of a geodesic
Euclidean space
Extrinsic curvature tensor
First groundform
Fluid film equations
Frenet formulas
Gauss’s theorem
Gauss’s Theorema Egregium
Gauss–Bonnet theorem
Gauss–Codazzi equation
Gaussian curvature
Genus of a closed surface
Geodesic
Gradient
Index juggling
Inner product matrix
Intrinsic derivative
Invariant
Invariant time derivative
Jolt of a particle
Kronecker symbol
Levi-Civita symbol
Mean curvature
Metric tensor
Metrics
Minimal surface
Normal derivative
Normal velocity
Orientation of a coordinate system
Orientation preserving coordinate change
Relative invariant
Relative tensor
Repeated index
Ricci tensor
Riemann space
Riemann–Christoffel tensor
Scalar
Scalar curvature
Second groundform
Shift tensor
Stokes’ theorem
Surface divergence
Surface Laplacian
Surge of a particle
Tangential coordinate velocity
Tensor property
Theorema Egregium
Third groundform
Thomas formula
Time evolution of integrals
Torsion of a curve
Total curvature
Variant
Vector
Parallelism along a curve
Permutation symbol
Polar coordinates
Position vector
Principal curvatures
Principal normal
Quotient theorem
Radius vector
Rayleigh quotient
Rectilinear coordinates
Vector curvature normal
Vector curvature tensor
Velocity of an interface
Volume element
Voss–Weyl formula
Weingarten’s formula
Applications: Differenital Geometry, Relativity

Views: 4576
MathTheBeautiful

When are vectors orthogonal? In this video you will learn about the innerproduct of vectors. With the inner product you can determine if vectors are orthogonal. You will also learn important properties of inner products. This prelecture video is part of the linear algebra courses taught at TU Delft.

Views: 3718
Mathematics TU Delft

#the mathematics world
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Thanks For watching..

Views: 1019
The Mathematics World

Views: 683
Jeff Suzuki

THANKS FOR WATCHING
INNER PRODUCT SPACE OF LINEAR ALGEBRA,
NORM OF VECTOR SPACE ,
FIND NORM ONE,
NORM TWO ,
NORM INFINITY,
contact:
[email protected]
moryamaths.blogspot.com
www.twitter.com/@morya2015 ( follow here)

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AMMATHS TUTORIALS

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Prasad Senesi

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refrigeratormathprof

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