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Coding For Physics Majors: Dot Products In Python
 
10:02
If you're new to coding, it might not be clear how to tie together things like calling functions, looping, and using arrays simultaneously. In this video I show you how to write a code to perform a dot product on two vectors using all of those aspects.
Views: 4819 Andrew Dotson
The difference between the dot product, and the inner product.
 
09:54
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: 8346 JJtheTutor
Dot product of two arrays using Python
 
04:56
This is a simple python program for finding the dot product of two arrays. Checkout the code on GitHub: https://github.com/shah78677/python-programs
Views: 68 Shah Quadri
Linear Algebra - Cosine & dot product
 
05:54
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: 1353 intrigano
Numpy Tutorial 5 Introduction to Dot Product
 
40:23
Introduction to dot products. Using the dot product to find what side of an arbitrarily rotated plane we're on.
Views: 407 Rich Colburn
Dot product 1: For loop vs. cosine method vs. dot function
 
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Deep Learning Prerequisites: The Numpy Stack in Python https://deeplearningcourses.com
Views: 788 Lazy Programmer
NumPy Tutorials : 007 : Vector and Matrix Operations
 
14:40
Do fill this form for feedback: Forum open till 23rd November 2017 https://docs.google.com/forms/d/1qiQ-cavTRGvz1i8kvTie81dPXhvSlgMND16gKOwhOM4/ All the programs and examples will be available in this public folder! https://www.dropbox.com/sh/okks00k2xufw9l3/AABkbbrfKetJPPsnfYa5BMSNa?dl=0 You can get the files via github from this link: https://github.com/arunprasaad2711 Follow me in Facebook and twitter: Facebook: http://www.facebook.com/arunprasaad2711 Twitter: http://www.twitter.com/arunprasaad2711 Dropbox link does not work! Website: http://fluidiccolours.in/ GitHub: https://github.com/arunprasaad2711/
Views: 1715 Fluidic Colours
Matrices and Vectors with Python | Create Row Vector, Column Vector | Calculate Dot Product - P9
 
03:07
''' Matrices and Vector with Python Topic to be covered - 1. Create a Vector 2. Calculate the Dot Product of 2 Vectors. ''' import numpy as np row_vector = np.array([1,4,7]) column_vector = np.array([[2], [5], [9]]) # Calcualte the Dot Product row_vector1 = np.array([3,6,8]) # Method 1 print(np.dot(row_vector,row_vector1)) # Method 2 print(row_vector @ row_vector1)
Linear Algebra Python - Finding vector au+bv and  the dot product of u and v.
 
19:38
In this video I will show you various operation on vector such as : 1) Enter a vector u as a n-list. 2) Enter another vector v as a n-list. 3) Find the vector au+bv for different values of a and b. 4) Find the dot product of u and v.
Views: 74 HashTech Coders
NumPy Tutorial 4(Transpose, Dot Multiplication, Vstack, Hstack, Flatten and Masking)
 
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In this video we wrap things up for the numpy basics and cover the transpose, dot multiplication, vstack, hstack and flatten/ravel. If you would like to dive deeper into the details of NumPy I highly recommend going through the documentation starting here https://docs.scipy.org/doc/numpy-dev/user/quickstart.html
Views: 1260 Ryan Chesler
Mathematics - PCA - Dot product
 
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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: 594 intrigano
Dot product 2: Speed comparison
 
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Deep Learning Prerequisites: The Numpy Stack in Python https://deeplearningcourses.com
Views: 579 Lazy Programmer
The Dot Operator in Python and ArcPy
 
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How to use the dot operator in ArcPy and Python.
Views: 2152 Richard Smith
Matrix Operations in Python - How to Use Numpy Matrices
 
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ACCESS the COMPLETE PYTHON TRAINING here: https://academy.zenva.com/product/python-mini-degree/?zva_src=youtube-python-md In this course we’ll be building a photo filter editor which allows you to create filters such as those used in Instagram and Snapchat. This app allows you to load a photo, edit it’s contrast, brightness and gray-scale. You can also create and apply custom filters using this tool. Theory sections are included, where concepts such as matrices, color models, brightness, contrast and convolution are explained in detail from a mathematical perspective. Practical sections include the installation of Virtual Box, matrix operations using Numpy, OpenCV and the libraries we’ll be using. Also, the photo editor is built from scratch using OpenCV UI. Learning goals: Matrices Color Models Brightness and Contrast Convolution OpenCV UI Our tutorial blogs: GameDev Academy: https://gamedevacademy.org HTML5 Hive: https://html5hive.org Android Kennel: https://androidkennel.org Swift Ludus: https://swiftludus.org De Idea A App: https://deideaaapp.org Twitter: @ZenvaTweets
Views: 10947 Zenva
Matrix Multiplication in Python
 
07:00
In this video, you will learn the fundamental concept of matrix multiplication from scratch. You can find the code in the Github link below: https://github.com/mohendra/My_Projects/tree/master/python
Views: 5022 AI Medicines
Internship Update And How To Take A Dot Product Of Two Four-Vectors
 
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Didn't have quite enough to talk about today, so I spontaneously broke into doing some math #wilin. I show how to take the inner product of a four vector with itself using the metric tensor, and relate it to the dot product of a regular vector! The notation I use is not meant to be extremely rigorous (with respect to contravariant and covariant indices), I mainly use it to keep track of row and column vectors.
Views: 2463 Andrew Dotson
#27 Python Tutorial for Beginners | Working with Matrix in Python
 
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Matrix Multiplication Theory : https://goo.gl/omPVAS Watch till 7:12 mins Python Tutorial to learn Python programming with examples Complete Python Tutorial for Beginners Playlist : https://www.youtube.com/watch?v=hEgO047GxaQ&t=0s&index=2&list=PLsyeobzWxl7poL9JTVyndKe62ieoN-MZ3 Python Tutorial in Hindi : https://www.youtube.com/watch?v=JNbup20svwU&list=PLk_Jw3TebqxD7JYo0vnnFvVCEv5hON_ew Editing Monitors : https://amzn.to/2RfKWgL https://amzn.to/2Q665JW https://amzn.to/2OUP21a. Editing Laptop : ASUS ROG Strix - (new version) https://amzn.to/2RhumwO Camera : https://amzn.to/2OR56AV lens : https://amzn.to/2JihtQo Mics https://amzn.to/2RlIe9F https://amzn.to/2yDkx5F Check out our website: http://www.telusko.com Follow Telusko on Twitter: https://twitter.com/navinreddy20 Follow on Facebook: Telusko : https://www.facebook.com/teluskolearnings Navin Reddy : https://www.facebook.com/navintelusko Follow Navin Reddy on Instagram: https://www.instagram.com/navinreddy20 Subscribe to our other channel: Navin Reddy : https://www.youtube.com/channel/UCxmkk8bMSOF-UBF43z-pdGQ?sub_confirmation=1 Telusko Hindi : https://www.youtube.com/channel/UCitzw4ROeTVGRRLnCPws-cw?sub_confirmation=1 Donation: PayPal Id : navinreddy20 Patreon : navinreddy20 http://www.telusko.com/contactus
Views: 58533 Telusko
Linear Algebra – Introduction Einstein summation convention and the symmetry of the dot product
 
09:54
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://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 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
Views: 981 intrigano
Dot product of two vectors: Example
 
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Learn via an example what is the dot product of two vectors. For more videos and resources on this topic, please visit http://ma.mathforcollege.com/mainindex/02vectors/
Views: 13463 numericalmethodsguy
Physics - Mechanics: Vectors (12 of 21) Product Of Vectors: Dot Product
 
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Visit http://ilectureonline.com for more math and science lectures! In this video I will explain the products of vectors, or dot product, or scalar product. Next video in this series can be seen at: https://youtu.be/ffAzfVYgDII
Views: 70193 Michel van Biezen
Using Dot Product to Find the Angle Between Two Vectors
 
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Using Dot Product to Find the Angle Between Two Vectors. You can use one of the dot product formulas to actually compute the angle between two vectors. Here we show how to use this formula to find an angle theta between two vectors Subscribe on YouTube: http://bit.ly/1bB9ILD Leave some love on RateMyProfessor: http://bit.ly/1dUTHTw Send us a comment/like on Facebook: http://on.fb.me/1eWN4Fn
Views: 39391 Firefly Lectures
16.Dot product of two vectors
 
01:16
Quick videos to perform Vector and Matrix operations using Python - Nitin Kaushik ***********Git Hub Link for Source Code************ https://github.com/nitinkaushik01/Matrix_and_Vector_Operations
Algorithms in CUDA: dot product
 
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Here we look at computing the dot product of two arrays using the GPU bitbucket repository: https://bitbucket.org/jsandham/algorithms_in_cuda
Views: 755 James Sandham
How to find the angle between two vectors
 
03:07
Learn how to determine the angle between two vectors. To determine the angle between two vectors you will need to know how to find the magnitude, dot product and inverse cosine. Then, the angle between two vectors is given by the inverse cosine of the ratio of the dot product of the two vectors and the product of their magnitudes. #trigonometry#vectors #vectors
Views: 70968 Brian McLogan
GG413: Matrix Addition, Vector Dot Product, Matrix Multiplication
 
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University of Hawaii, Dept. of Geology & Geophysics, Garrett Apuzen-Ito, GG413: Geological Data Analysis www.soest.hawaii.edu/GG/FACULTY/ITO/GG413
Views: 2334 Garrett Apuzen-Ito
Coding a visualization of dot products in OpenGL/GLSL
 
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We code a visualization of dot products (and vector projections!) in GLSL, the OpenGL Shading Language, using both the rectangular representation of vectors and the polar representation of vectors. Shadertoy code: https://www.shadertoy.com/view/4lXyRf Intro music, Axl Rosenberg's Ascendance: https://www.youtube.com/watch?v=_3sHLVtLe5U Subscribe for more content! =)
Views: 2974 mathIsART
Dot Product
 
08:18
A short introduction to the dot product
Views: 46 vigvig
Element by Element Multiplication in Matrices (Dot Product)
 
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Visualisation of elements in a Matrix as objects. Description of Element by Element Multiplication of Matrices (Dot Product) Using MATLAB. Written notes: https://dellwindowsreinstallationguide.com/element-by-element-operations-multiplication/
Views: 11 Philip Yip
Mathematical devices in Deep Learning II - Matrix Dot Product
 
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In the "Mathematical devices in deep learning II, matrix dot product is covered
Views: 526 Vasu Srinivasan
Linear Algebra using Python - Finding vector –matrix multiplication &  matrix-matrix product
 
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In this video I will show you vector and matrix multiplications in following ways : 1) Find the vector –matrix multiplication of a r by c matrix M with an c-vector u.  2) Find the matrix-matrix product of M with a c by p matrix N.
Views: 81 HashTech Coders
Vector Projection and Dot Product
 
05:23
We can project a vector onto another vector using the dot product. This tells us what portion of the first vector is parallel to the vector we are projecting onto. In games, we use this trick in several areas (graphics, physics, etc.)
Views: 1770 Jamie King
Dot Products and Ship vs Wall Collision
 
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If the SIGN of the dot product goes negative, then the angle between two vectors is greater than 90 degrees (PI/2), which means the ship has crossed the wall boundary.
Views: 822 Jamie King
Support Vector Machines - The Math of Intelligence (Week 1)
 
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Support Vector Machines are a very popular type of machine learning model used for classification when you have a small dataset. We'll go through when to use them, how they work, and build our own using numpy. This is part of Week 1 of The Math of Intelligence. This is a re-recorded version of a video I just released a day ago (the audio/video quality is better in this one) Code for this video: https://github.com/llSourcell/Classifying_Data_Using_a_Support_Vector_Machine Please Subscribe! And like. And comment. that's what keeps me going. Course Syllabus: https://github.com/llSourcell/The_Math_of_Intelligence Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ More Learning resources: https://www.analyticsvidhya.com/blog/2015/10/understaing-support-vector-machine-example-code/ http://www.robots.ox.ac.uk/~az/lectures/ml/lect2.pdf http://machinelearningmastery.com/support-vector-machines-for-machine-learning/ http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf http://www.statsoft.com/Textbook/Support-Vector-Machines https://www.youtube.com/watch?v=_PwhiWxHK8o And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 157248 Siraj Raval
Fundamentals - The Dot Product
 
14:16
Vectors and normals are extremely important for CG production. These data types support several operations, one of which is called the Dot Product. The Dot Product is one of the most useful operations in CG as it can provide you with facing ratios between two vector types. Give this video a look and learn how the dot product is calculated and how it can be used in production.
Views: 878 TDChannel
Mathematics for Machine Learning full Course || Linear Algebra || Part-1
 
03:49:06
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 Topic covered: Solving data science challenges with mathematics Motivations for linear algebra Getting a handle on vectors Operations with vectors Modulus & inner product Cosine & dot product Projection Changing basis Basis, vector space, and linear independence Applications of changing basis Matrices, vectors, and solving simultaneous equation problems How matrices transform space Types of matrix transformation Composition or combination of matrix transformations Solving the apples and bananas problem: Gaussian elimination Going from Gaussian elimination to finding the inverse matrix Determinants and inverses Einstein summation convention and the symmetry Matrices changing basis Doing a transformation in a changed basis Orthogonal matrices The Gram–Schmidt process Gram-Schmidt process What are eigenvalues and eigenvectors? Special eigen-cases Calculating eigenvectors Changing to the eigenbasis Eigenbasis example Introduction to PageRank ****************************************************************** This course is created by Imperial College London If you like this video and course explanation feel free to take the complete course and get certificate from: https://www.coursera.org/specializations/mathematics-machine-learning This video is provided here for research and educational purposes in the field of Mathematics. No copyright infringement intended. If you are content owner would like to remove this video from YouTube, Please contact me through email: [email protected] *******************************************************************
Views: 122840 Geek's Lesson
How to calculate an orthogonal vector
 
08:35
Here a short tutorial combining Python, Cinema 4d and vectors. I explain how to get the orthogonal vector that bisect another line perpendicularly in the XZ plane. Please have a look on the Internet for the mathematics of orthogonal vectors, Dot product, etc. You can download the source from my blog.grooff.eu
Views: 897 Pim Grooff
Element Wise Multiplication in Python Numpy
 
01:54
Test your skills in element-wise matrix multiplication in Python Numpy: https://blog.finxter.com/python-numpy-element-wise-multiplication/ Join my 5,500+ rapidly growing Python community -- and get better in Python on auto-pilot! http://bit.ly/free-python-course It's fun! :)
Ex: Dot Product of Vectors From a Graph - 2D
 
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This video provides several examples of how to determine the dot product of vectors in two dimensions and discusses the meaning of the dot product. Site: http://mathispower4u.com
Views: 2735 Mathispower4u
Graphing in Space -- Distance and The Dot Product
 
57:55
Filmed on Wednesday August 9, 2017
Views: 10 englematics

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