Search results “Dot product python”

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

Views: 10804
Vidya Sagar

Views: 12395
Deeplearning.ai

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

Views: 1067
Abraham Smith

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

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
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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

Introduction to dot products. Using the dot product to find what side of an arbitrarily rotated plane we're on.

Views: 407
Rich Colburn

Deep Learning Prerequisites: The Numpy Stack in Python
https://deeplearningcourses.com

Views: 788
Lazy Programmer

Do fill this form for feedback: Forum open till 23rd November 2017
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Views: 1715
Fluidic Colours

'''
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)

Views: 351
MachineLearning with Python

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

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

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
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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

Deep Learning Prerequisites: The Numpy Stack in Python
https://deeplearningcourses.com

Views: 579
Lazy Programmer

How to use the dot operator in ArcPy and Python.

Views: 2152
Richard Smith

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.
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Color Models
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Convolution
OpenCV UI
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Views: 10947
Zenva

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

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

Matrix Multiplication Theory : https://goo.gl/omPVAS
Watch till 7:12 mins
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Views: 58533
Telusko

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

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

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. 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
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Views: 39391
Firefly Lectures

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

Views: 3
The AI University

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

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

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

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

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

In the "Mathematical devices in deep learning II, matrix dot product is covered

Views: 526
Vasu Srinivasan

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

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

Views: 218
ElPoloDeNolo

Views: 113
Noah Wang

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 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)
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http://machinelearningmastery.com/support-vector-machines-for-machine-learning/
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Views: 157248
Siraj Raval

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

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

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

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! :)

Views: 90
Finxter - Coffee Break Python

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

Filmed on Wednesday August 9, 2017

Views: 10
englematics

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© 2019 Drunk and sleeping girls video

The Headache Paradox: It is scientifically impossible to have a headache, seeing that there are no pain receptors in the brain. Yet we continue to search for answers to what causes a headache, because we all know that headaches do exist. Between a combination of the fact that our brains are roughly nothing more than a highly advanced mass of nerves, and that every function is controlled by signals that our brain sends through our nerves to the rest of our bodies, I think that if we could somehow make our nerves send stronger, more efficient signals at a quicker speed it would have a lot of different results. Including heightened sense (both physical and psychic) moving quicker both running and walking along with arm movement etc. The brain being just nerves would obviously also be effected, resulting in quicker more efficient thinking, which naturally would make us smarter, more observant, and have much sharper reflexes. Some say that is an overly simplified form of how the brain functions, which it is. To them I say make the axons 12-15% wider, resulting in signals being sent much more quickly through the brain. Allowing neurons to create more connections without loss of speed, and the 10,000+ miles of blood vessels in the brain doubling as an additional cooling system for the brain. I could write a book on the cascade of effects that alone causes for people when they are changed in to a real vampire, however for now I will leave it in this simplified form.