The vector space ν with an inner product is called a (real) inner product space.
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About Chris W., Math tutor on Chegg Tutors:
University of Pennsylvania, Class of 2007
Math, Computer Science major
Subjects tutored: Applied Mathematics, Geometry, Web Design, Numerical Analysis, GRE, Linear Algebra, LaTeX, Calculus, SAT II Mathematics Level 2, SSAT (math), ACT (math), Computer Science, Linear Programming, Basic Math, SAT (math), Geometry (College Advanced), Pre-Calculus, Statistics, Computer Certification and Training, Algebra, Software Engineering, Information Technology, PSAT (math), Discrete Math, Number Theory, SAT II Mathematics Level 1, Pre-Algebra, Trigonometry, and Set Theory
TEACHING EXPERIENCE
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.
EXTRACURRICULAR INTERESTS
Hiking, reading, video games, playing the guitar and piano.
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Views: 44543
Chegg

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: 142742
patrickJMT

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

Views: 37870
nptelhrd

Bessel's inequality in hindi. Bessel's inequality in inner product space. Bessel's inequality for orthonormal set.
Orthonormal set.
Gram-Schmidt Orthogonalization Process https://youtu.be/TdH2wd-Ynrc
Inner product space.
Linear Algebra.
ABSTRACT ALGEBRA.
Please subscribe the chanel for more vedios and please support us 🙏.

Views: 10239
Mathematics Analysis

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: 8149
Betty Love

(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: 2509
PhysicsHelps

شرح مادة جبر خطي مع الطالب حمزة السيلاوي
- لمعلومات أكثر يرجى زيارة موقع الفريق : http://itplusteam.com/
- و للإنضمام لجروب الفريق الرئيسي على الفيسبوك : https://web.facebook.com/groups/ItPlu... ...
#it_plus
#اقوى_فريق
www.itplusteam.com

Views: 2398
itplus team

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

Views: 28726
nptelhrd

https://bit.ly/PG_Patreon - Help me make these videos by supporting me on Patreon!
https://lem.ma/LA - Linear Algebra on Lemma
https://lem.ma/prep - Complete SAT Math Prep
http://bit.ly/ITCYTNew - My Tensor Calculus Textbook

Views: 16889
MathTheBeautiful

Inner product space solved examples.
Inner product space solved problems.
Inner product space in hindi.
Solved problems of distance and orthogonality.
Inner product space.
#MathematicsAnalysis.
Please subscribe the chanel for more vedios and please support us.

Views: 2450
Mathematics Analysis

Views: 849
Jeff Suzuki

https://bit.ly/PG_Patreon - Help me make these videos by supporting me on Patreon!
https://lem.ma/LA - Linear Algebra on Lemma
https://lem.ma/prep - Complete SAT Math Prep
http://bit.ly/ITCYTNew - My Tensor Calculus Textbook

Views: 1856
MathTheBeautiful

Advanced Numerical Analysis by Prof. Sachin C. Patwardhan,Department of Chemical Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.ac.in

Views: 7083
nptelhrd

Hope this makes sense and that i am explaining well. if problems please comment

Views: 18574
Matt B

Views: 1368
Iyad Obeid

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: 7907
nptelhrd

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
Linear transformation - Linear Transformation - complete concept & fully solved questions in hindi: https://www.youtube.com/playlist?list=PLtFV0hYqGnElZ6QJojALui-zhDrbRiIqs
Please subscribe the chanel for more vedios and please support us.

Views: 20482
Mathematics Analysis

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: 13610
Lorenzo Sadun

Mathematics for Machine Learning: Linear Algebra, Module 2 Vectors are objects that move around space
To get certificate subscribe at: https://www.coursera.org/learn/linear-algebra-machine-learning/home/welcome
============================
Mathematics for Machine Learning: Linear Algebra:
https://www.youtube.com/playlist?list=PL2jykFOD1AWazz20_QRfESiJ2rthDF9-Z
============================
Youtube channel: https://www.youtube.com/user/intrigano
============================
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 2 Vectors are objects that move around space
In this module, we look at operations we can do with vectors - finding the modulus (size), angle between vectors (dot or inner product) and projections of one vector onto another. We can then examine how the entries describing a vector will depend on what vectors we use to define the axes - the basis. That will then let us determine whether a proposed set of basis vectors are what's called 'linearly independent.' This will complete our examination of vectors, allowing us to move on to matrices in module 3 and then start to solve linear algebra problems.
Less
Learning Objectives
• Calculate basic operations (dot product, modulus, negation) on vectors
• Calculate a change of basis
• Recall linear independence
• Identify a linearly independent basis and relate this to the dimensionality of the space

Views: 1510
intrigano

Section 6.1. Inner Product, Length and Orthogonality

Views: 270
Mihran Papikian

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: 470
intrigano

Definition of an inner product and some examples

Views: 38360
Gilbert Eyabi

Views: 16335
refrigeratormathprof

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: 366
intrigano

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: 788
James Hamblin

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

Views: 8622
nptelhrd

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

Views: 7141
ANS ACADEMY

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: 354
intrigano

Dot products are a nice geometric tool for understanding projection. But now that we know about linear transformations, we can get a deeper feel for what's going on with the dot product, and the connection between its numerical computation and its geometric interpretation.
Full series: http://3b1b.co/eola
Future series like this are funded by the community, through Patreon, where supporters get early access as the series is being produced.
http://3b1b.co/support
------------------
3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted about new videos, subscribe, and click the bell to receive notifications (if you're into that).
If you are new to this channel and want to see more, a good place to start is this playlist: https://goo.gl/WmnCQZ
Various social media stuffs:
Website: https://www.3blue1brown.com
Twitter: https://twitter.com/3Blue1Brown
Patreon: https://patreon.com/3blue1brown
Facebook: https://www.facebook.com/3blue1brown
Reddit: https://www.reddit.com/r/3Blue1Brown

Views: 553618
3Blue1Brown

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: 430
John Harland

Please watch: "Group Theory - Lagrange's theorem in Hindi | GATE | CSIR NET | B.Sc Preparation"
https://www.youtube.com/watch?v=CiV2wUm1Nkc --~--
#the mathematics world
Please Subscribe Your Channel THE MATHEMATICS WORLD.
Click Here for Subscribe : http://bit.ly/2gHVkBm
Here Are Some other videos :
Projection of Linear Transformation in Hindi | Linear Algebra : https://bit.ly/2rsVqOE
Shortcut method for Linear Equation : http://bit.ly/2AJmJuG
Linear equation( Consistent and inconsistent System ) in Hindi Part -2 : http://bit.ly/2iM7nOS
Linear equation( Consistent and inconsistent System ) in Hindi Part -1 : https://youtu.be/bU9vkEYyJ8E
hermitian and skew harmition matrix : http://bit.ly/2y60yto
How to find Rank of a matrix in Hindi : http://bit.ly/2y3Nw4v
Symmetric and skew symmetric Matrices : http://bit.ly/2iwDfXl
Nilpotent, Idempotent, Involuntary Matrix : http://bit.ly/2gDKb0x
Orthogonal matrix in Hindi : http://bit.ly/2ixRJGm
Multiplication of matrices With Same And Different Order in hindi :
http://bit.ly/2h8BAUu
Find Determinent Of Matrix in Hindi : http://bit.ly/2yKXnJh
Find Inverse of a matrix by a shortcut method: http://bit.ly/2yNfqAg
Vandermode matrix With Example in Hindi : http://bit.ly/2yM69qF
Doubly Stochastic Matrix With Example in Hindi : http://bit.ly/2yMynS2
How to calculate Eigen values in matrix - Hindi : http://bit.ly/2y4JQiQ
Thanks For watching..

Views: 4708
The Mathematics World

Views: 316
intrigano

please share and subscribe my chanal for more video

Views: 3511
CSIR-NET MATHEMATICS

Distance in inner product space in hindi.
Distance in inner product space theorem proof.
Distance in inner product space examples.
Inner product space.
Please subscribe the chanel for more vedios and please support us.

Views: 425
Mathematics Analysis

Norms and inner product spaces

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: 1101
Professor Macauley

We define and explain the properties of the inner product to allow for the specification of angles between vectors in high dimensions, and use the notion of orthogonality (angle =90deg) to easily solve linear equations

Views: 3212
William Nesse

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: 2597
nptelhrd

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

Views: 4247
nptelhrd

This is a test video. More videos will come.
Note: Error in my first coefficient (ao)...it should be half what I stated. I won't have access to a whiteboard before the next lec

Views: 581
Joseph Tunji Onikoyi

Views: 1083
Iyad Obeid

IF U WANTS QUALIFY CSIR NET MATHEMATICS ...DONT WORRY WE WILL HELP U... AND WE WILL MAKE LOTS OF VIDEO ON EACH TOPIC WHICH IS SO IMP FOR CSIR NET...FOR MOR INFORMATION U WILL CALL US AT 8607383607

Views: 4222
CSIR-NET MATHEMATICS

NPTEL 30: Mathematics Maintain By NPTEL (Mathematics)

Views: 85
Ch 30 NIOS: Gyanamrit

https://bit.ly/PG_Patreon - Help me make these videos by supporting me on Patreon!
https://lem.ma/LA - Linear Algebra on Lemma
https://lem.ma/prep - Complete SAT Math Prep
http://bit.ly/ITCYTNew - My Tensor Calculus Textbook

Views: 2400
MathTheBeautiful

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: 2682
nptelhrd

Views: 343
Erik Nelson