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: 981
Prof Hoekstra

Views: 11326
Deeplearning.ai

Do fill this form for feedback: Forum open till 23rd November 2017
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All the programs and examples will be available in this public folder!
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Views: 1484
Fluidic Colours

Views: 9765
Vidya Sagar

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: 2350
Jeffrey Chasnov

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: 7745
JJtheTutor

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: 501
Rich Colburn

This introductory homework assignment solution covers Numpy and loops (for and while) in Python. The example problems use simple vectors and matrices, reshaping, index referencing, initialization, dot product, cross product, matrix inverse, size, and range.

Views: 5652
APMonitor.com

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: 4175
Andrew Dotson

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

Views: 380
Rich Colburn

In this tutorial, we cover some basics on vectors, as they are essential with the Support Vector Machine.
https://pythonprogramming.net
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Views: 57737
sentdex

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):
https://github.com/llSourcell/Vectors_Linear_Algebra/tree/master
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https://github.com/Sri-Vishnu-Kumar-K/MathOfIntelligence/blob/master/second_order_optimization_newtons_method/second_order_optimization.py
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https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/blob/master/Newtons%20Method.ipynb
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More learning resources:
http://mathworld.wolfram.com/VectorNorm.html
http://www.math.usm.edu/lambers/mat610/sum10/lecture2.pdf
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: 82085
Siraj Raval

'''
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: 311
MachineLearning with Python

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: 739087
patrickJMT

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:
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Swift Ludus: https://swiftludus.org
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Twitter: @ZenvaTweets

Views: 10264
Zenva

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: 62
Shah Quadri

Views: 217
ElPoloDeNolo

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

Views: 30692
Udacity

Views: 1015
Abraham Smith

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

Views: 12
Nook Tutorials

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: 5621
MathTheBeautiful

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/
https://www.youtube.com/c/AhmadBazzi
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Views: 9363
Ahmad Bazzi

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
Are you using a textbook? Check out our website for videos organized by textbook chapters: http://www.learncheme.com/screencasts

Views: 15625
LearnChemE

alternating between sympy and numpy doing complex number multiplication, matrix vector products, matrix matrix products, matrix element by element products

Views: 8
MrProfScott

Code to compute the product of all values from a matrix.
Like and share. It's FREE too :)
Download source code at:
https://drive.google.com/file/d/1GdeiAIASsZFjiUUJ-JoEe3HVTXv6_Ttz/
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Views: 32
AllTech

Views: 112
Noah Wang

( 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:
1. What is Numpy?
2. Numpy v/s Lists
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:
1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
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Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
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.
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
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Views: 180102
edureka!

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:
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Views: 149838
Siraj Raval

Views: 1227
Team Technology

Given an LTI system impulse response h[n], convolve each of four finite-length sequences with h[n] to determine the output sequence y[n].
** See the full collection of problems and tutorials at http://www.rose-hulman.edu/~doering/ece380_tutorials_and_problems.pdf **

Views: 123595
Rose-Hulman Online

The whole of numpy is based on arrays. You need to know numpy in order to do vector transformations in machine learning. Below are the links mentioned in the video.
https://medium.com/technology-nineleaps/vectors-in-machine-learning-b8dbdae53aa0
http://www.scipy-lectures.org/intro/numpy/numpy.html
https://www.tutorialspoint.com/numpy/numpy_reshape.htm
https://stackoverflow.com/a/31181358/5417164
http://ml-cheatsheet.readthedocs.io/en/latest/linear_algebra.html

Views: 183
joydeep bhattacharjee

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
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Views: 51075
Telusko

A short introduction to Numpy arrays (np.array) in this Learn Data Science with Python course. Numpy is a very powerful linear algebra and matrix package for python. It's very useful when doing data science with python. Here I give you a brief overview of numpy and how it works. We look at arrays in numpy, ndim, shape and size methods on arrays.
If this has been useful, then consider giving your support by buying me a coffee https://ko-fi.com/pythonprogrammer
If you want to learn python, I have a free course here on my YouTube channel https://www.youtube.com/playlist?list=PLtb2Lf-cJ_AWhtJE6Rb5oWf02RC2qVU-J
Here's the link to the image:-
https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Parliament_at_Sunset.JPG/800px-Parliament_at_Sunset.JPG

Views: 3489
Python Programmer

Visit my personal web-page for the Python code:
http://www.brunel.ac.uk/~csstnns

Views: 5843
Noureddin Sadawi

Visit my personal web-page for the Python code:
http://www.brunel.ac.uk/~csstnns

Views: 4997
Noureddin Sadawi

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

Views: 583
Lazy Programmer

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: 1183
IT Connected

Views: 77219
ritvikmath

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

Views: 5246
Rhett Allain

Backpropagation as simple as possible, but no simpler. Perhaps the most misunderstood part of neural networks, Backpropagation of errors is the key step that allows ANNs to learn. In this video, I give the derivation and thought processes behind backpropagation using high school level calculus.
Supporting Code and Equations:
https://github.com/stephencwelch/Neural-Networks-Demystified
In this series, we will build and train a complete Artificial Neural Network in python. New videos every other friday.
Part 1: Data + Architecture
Part 2: Forward Propagation
Part 3: Gradient Descent
Part 4: Backpropagation
Part 5: Numerical Gradient Checking
Part 6: Training
Part 7: Overfitting, Testing, and Regularization
@stephencwelch

Views: 374739
Welch Labs

In mathematics, matrix multiplication or matrix product is a binary operation that produces a matrix from two matrices with entries in a field, or, more generally, in a ring. The matrix product is designed for representing the composition of linear mapsthat are represented by matrices. Matrix multiplication is thus a basic tool of linear algebra, and as such has numerous applications in many areas of mathematics, as well as in applied mathematics, physics, and engineering.[1][2] In more detail, if A is an n × m matrix and B is an m × p matrix, their matrix product AB is an n × p matrix, in which the m entries across a row of A are multiplied with the m entries down a column of B and summed to produce an entry of AB. When two linear maps are represented by matrices, then the matrix product represents the composition of the two maps.
The definition of matrix product requires that the entries belong to a ring, which may be noncommutative, but is a field in most applications. Even in this latter case, matrix product is not commutative in general, although it is associative and is distributiveover matrix addition. The identity matrices(which are the square matrices whose all entries are zero, except those of the main diagonal that are all equal to 1) are identity elements of the matrix product. It follows that the n × n matrices over a ring form a ring, which is noncommutative except if n = 1 and the ground ring is commutative.
A square matrix may have a multiplicative inverse, called an inverse matrix. In the common case where the entries belong to a commutative ring r, a matrix has an inverse if and only if its determinant has a multiplicative inverse in r. The determinant of a product of square matrices is the product of the determinants of the factors. The n × nmatrices that have an inverse form a groupunder matrix multiplication, the subgroups of which are called matrix groups. Many classical groups (including all finite groups) are isomorphic to matrix groups; this is the starting point of the theory of group representations.
Computing matrix products is a central operation in all computational applications of linear algebra. Its computational complexity is {\displaystyle O(n^{3})}￼ (for n × n matrices) for the basic algorithm (this complexity is {\displaystyle O(n^{2.373})}￼ for the asymptotically fastest known algorithm). This nonlinear complexity means that matrix product is often the critical part of many algorithms. This is enforced by the fact that many operations on matrices, such as matrix inversion, determinant, solving systems of linear equations, have the same complexity. Therefore various algorithms have been devised for computing products of large matrices, taking into account the architecture of computers (see BLAS, for example).
To watch all Python programs,
Visit my channel 👇
https://www.youtubecom/channel/UCkktsFQAPJz8PkMr15gAhXw
Or
www.youtube.com/channel/Pratik Matkar

Views: 4342
Pratik Matkar

np.hstack() is a numpy function using two or more arrays that allows you to combine arrays and make them into one array. Hstack stands for horizontal stack. This video explains how to use python numpy hstack function on arrays / matrices.
This is a Python anaconda tutorial for help with coding, programming, or computer science. These are short python videos dedicated to troubleshooting python problems and learning Python syntax. For more videos see Python Help playlist by Rylan Fowers.
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How to use np.hstack in python
we import numpy as np
And now we will create some arrays to demonstrate with.
To create an array type np.array, parentheses, bracket to start the matrix, and a bracket starting each row. End by closing the last bracket and parentheses.
We will press the up arrow on the keyboard to bring that up again, and we can edit it to make a matrix y
So here we have matrix x
and here is matrix y
we type np.hstack with parenthesis, and then you MUST make the entry a tuple, so do double parenthesis and put x comma y close close
Notice the x array is on the left and the y matrix is on the right since we put x first then y. h stack is horizontal stack. For it to work, both matrices must have the same amount of ROWS
So remember HR Hstack works when Rows line up.
There you have it, that is how you use Hstack in python

Views: 707
Rylan Fowers