Search results “Numpy vector product”

Using numpy array and numpy matrix for linear algebra, vectors, and matrices.
0:41 Dot product on 1D numpy arrays (=inner product of vectors)
1:50 Length of a vector: norm( ) function
2:23 Project vector a on vector b
5:17 Use 2D arrays as a matrix
6:05 Solve Ax=b
6:35 Use 2D array as a vector (column orientation)
7:33 Transpose a vector/matrix/2D array: .T method
8:38 Matrix multiplication with arrays: using .dot( ) on 2D arrays
11:38 Matrix type in numpy (Note: voice says A.Y where it has to say A.I !)
12:48 Matrix multiplication with matrix type: "*" (works also with column vectors)
Not covered, but worth checking out: numpy's cross(a,b) function, det( ) function from numpy.linalg

Views: 1544
Prof Hoekstra

Views: 15603
Deeplearning.ai

Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! In this video, I give the formula for the cross product of two vectors, discuss geometrically what the cross product is, and do an example of finding the cross product.
For more free math videos, visit http://PatrickJMT.com

Views: 759994
patrickJMT

Do fill this form for feedback: Forum open till 23rd November 2017
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Views: 2392
Fluidic Colours

Definition of an inner and outer product of two column vectors.
Take my Coursera course at
https://www.coursera.org/learn/matrix-algebra-engineers
Download lecture notes from
http://www.math.ust.hk/~machas/matrix-algebra-for-engineers.pdf

Views: 6743
Jeffrey Chasnov

Views: 12799
Vidya Sagar

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: 9630
JJtheTutor

This video is part of an online course, Intro to Parallel Programming. Check out the course here: https://www.udacity.com/course/cs344.

Views: 34837
Udacity

We look at how to use two different handlers inside blender for getting constant live updates. We show how to get vertex locations with modifier effects. We also talk about how to generate our own normals from the cross product.

Views: 608
Rich Colburn

For a complete course on machine learning do visit
https://www.udemy.com/demystifying-ma...
For a limited time, it is free

Views: 21
Data Science Mastery

Here is a quick intro to vector calculations using VPython.
https://trinket.io/glowscript/36bf2d2e8b

Views: 6855
Rhett Allain

Learn NumPy Linear Algebra in just ONE VIDEO !!
00:00:00 Intro
00:02:31 Jupyter setup
00:06:23 Numpy setup
00:08:16 Markdown cell
00:10:40 Array
00:11:26 type function
00:13:01 Indexing Array elements
00:14:36 Dimensions of Array
00:15:38 Matrix
00:17:36 Extracting a sub-matrix
00:19:22 Modifying matrix elements
00:22:15 Identity matrix
00:22:50 Zeros matrix
00:24:14 Ones matrix
00:24:48 Constant matrix
00:27:48 Random matrix
00:31:11 Mean
00:33:35 Standard Deviation
00:36:49 dtype function
00:38:31 Matrix Addition
00:41:06 Matrix Subtraction
00:41:45 Matrix Point-wise Multiplication
00:43:00 Matrix Point-wise Division
00:46:08 Matrix Products
00:46:44 np.matmul function
00:50:40 np.dot function
00:51:40 np.inner function
00:52:46 np.tensordot
00:55:52 Matrix Exponentiation
00:57:13 Kronecker Product
00:59:14 Matrix Decompositions
00:59:23 Cholesky Decomposition
01:03:06 QR Decomposition
01:05:05 EigenValue Decomposition (EVD)
01:08:58 SingularValue Decomposition (SVD)
01:10:08 Matrix Norms
01:10:10 L2 Frobenius Norm
01:10:24 Condition Number
01:10:56 Determinant of a matrix
01:11:10 Rank of a matrix
01:11:33 Trace of a matrix
01:13:05 Solving Linear Equations Ax = b
01:13:39 Inverse of a matrix
01:14:10 np.linalg.solve function
01:14:56 Moore-Penrose Pseudo-Inverse
01:15:53 Recap
Instructor: Dr. Ahmad Bazzi
IG: https://www.instagram.com/drahmadbazzi/
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NumPy: http://www.numpy.org/
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Views: 11721
Ahmad Bazzi

Views: 119
Noah Wang

Views: 85411
ritvikmath

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: 1491
Ryan Chesler

The dot function can be used to multiply matrices and vectors defined using NumPy arrays. The @ symbol can also be used for matrix multiplication in Python 3.5 and newer.

Views: 29
PyPros

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: 6430
Andrew Dotson

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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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Views: 12708
Zenva

We're going to explore why the concept of vectors is so important in machine learning. We'll talk about how they are used to represent both data and models. Get ready for some Linear Algebra!
Code for this video (with challenge):
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https://www.youtube.com/watch?v=tXCqr2UsbWQ
https://stackoverflow.com/questions/38379905/what-is-vector-in-terms-of-machine-learning
http://www.chioka.in/differences-between-the-l1-norm-and-the-l2-norm-least-absolute-deviations-and-least-squares/
https://www.quora.com/What-is-the-difference-between-L1-and-L2-regularization
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Views: 90471
Siraj Raval

Views: 1197
Abraham Smith

'''
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: 469
MachineLearning with Python

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

Views: 702
Lazy Programmer

Matrix Multiplication Theory : https://goo.gl/omPVAS
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Views: 90069
Telusko

In this tutorial, we cover some basics on vectors, as they are essential with the Support Vector Machine.
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Views: 63441
sentdex

Learn how to do Scalar Array Operation in Numpy Python.

Views: 656
DevNami

These videos were created to accompany a university course, Numerical Methods for Engineers, taught Spring 2013. The text used in the course was "Numerical Methods for Engineers, 6th ed." by Steven Chapra and Raymond Canale.

Views: 76138
Jacob Bishop

This Python Numpy Matrix 5 minute tutorial gives basics on Matrices, Arrays and basic operations on them.

Views: 12
Nook Tutorials

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

Views: 479
Rich Colburn

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: 208
Finxter - Coffee Break Python

( Python Training : https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn following topics:
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3. Numpy Operations
4. Numpy Special Functions
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Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
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Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
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Views: 227419
edureka!

Code to compute the product of an array.
Like and share. It's FREE too :)
Download source code at:
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Views: 31
AllTech

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

Views: 670
Lazy Programmer

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: 100
Shah Quadri

Explains element-wise multiplication (Hadamard product) and division of matrices. Part 3 of the matrix math series.
Made by faculty at the University of Colorado Boulder, Department of Chemical & Biological Engineering.
Check out our Engineering Computing playlists: https://www.youtube.com/user/LearnChemE/playlists?sort=dd&view=50&shelf_id=4
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Views: 18905
LearnChemE

Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
Reference:
https://class.coursera.org/ml-007

Views: 14759
Alan Saberi

Code to compute the product of all values from a matrix.
Like and share. It's FREE too :)
Download source code at:
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Views: 45
AllTech

'''
Matrices and Vector with Python
Topic to be covered - How to reshape a matrix?
'''
import numpy as np
matrix = np.random.randint(0,9,(6,6))
print(matrix.reshape(4,9))
print(matrix.reshape(9,4))
print(matrix.reshape(12,3))
print(matrix.reshape(3,12))

Views: 120
MachineLearning with Python

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
============================
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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: 752
intrigano

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: 1714
intrigano

Views: 235
ElPoloDeNolo

'''
Matrices and Vector with Python
Topic to be covered -
1. Find the Diagonal of a matrix
'''
import numpy as np
matrix = np.random.randint(0,9,(8,8))
print(matrix.diagonal())
print(matrix.diagonal().sum())
matrix1 = np.random.randint(0,9,(5,6))
print(matrix1.diagonal())

Views: 197
MachineLearning with Python

'''
Matrices and Vector with Python
Session # 1
Topic to be covered -
1. How to create Matrices
2. How to create random matrices of different orders
3. How to access the matrices elements
4. How to delete rows and column of a matrix.
'''
'''
Q) What is Matrix?
Matrix is a rectangular array of numbers, symbols or expression arranged in rows and columsn.
Q) Where do we use matrix in Machine Learning?
Matrix are used to read the input data which is in the form of .csv, .txt, .xml and other formats.
It is especially used to processed as the input data varible (X) when training the algorithm.
'''
import numpy as np
#1. How to create Matrices
matrix = np.array([[3,4],
[5,8]])
# How to create using random
#2. How to create random matrices of different orders
#import random
print(np.random.random((2,2)))
print(np.random.random((3,3)))
print(np.random.random_integers(0,9,(2,2)))
print(np.random.randint(0,100,(5,5)))
# 3. How to access the matrices elements
x = np.random.randint(0,100,(5,5))
# Extract the first column
x[:,0]
# Extract the Second Column
x[:,1]
# Extract the first row
x[0]
x[2]
# How to extract the 2nd and 4th row
x[[2,4]]
# How to extract the 1st and 4th Column
y = x[:,[1,4]]
##############################################################################
# 4. How to delete rows and column of a matrix.
# How to delete the second row
np.delete(x,[1],0)
# How to delete the second column
np.delete(x,[1],1)
# Delete second and third row
np.delete(x,[[2,3]],0)
# Delete second and third column
np.delete(x,[[2,3]],1)

Views: 516
MachineLearning with Python

This video deals with the definition of the dot product under the geometric viewpoint. The standard basis are also used to determine the dot product of two vectors.

Views: 1309
Carlos Thompson

'''
Matrices and Vector with Python
Topic to be covered - Maximum and Mininum Values of the Matrix
'''
import numpy as np
matrix_1 = np.random.randint(0,100,(6,6))
# 1. Maximum Value
print(np.max(matrix_1))
# 2 . Minimum Value
print(np.min(matrix_1))
# 3. Max and Min Vlue for each and every columns
print(np.max(matrix_1,axis=0))
print(np.min(matrix_1,axis=0))
# 4. Max and Min Vlue for each and every rows
print(np.max(matrix_1,axis=1))
print(np.min(matrix_1,axis=1))

Views: 162
MachineLearning with Python

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