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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
Views: 2797 Lazy Programmer
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
Views: 1215 Lazy Programmer
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 https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 57737 sentdex
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([, , ]) # 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)
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: 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: 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: 2659 Team Technology
I discuss tensor products.
Views: 57383 Jim Fowler
Views: 217 ElPoloDeNolo
Views: 66 Abraham Smith
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/ Browser: https://www.google.com/chrome/ NumPy: http://www.numpy.org/ https://www.youtube.com/c/AhmadBazzi ●▬▬▬▬▬▬▬๑۩۩๑▬▬▬▬▬▬▬▬● _*****╔═╦╗╔╦╗╔═╦═╦╦╦╦╗╔═╗***** _ _*****║╚╣║║║╚╣╚╣╔╣╔╣║╚╣═╣***** _ _*****╠╗║╚╝║║╠╗║╚╣║║║║║═╣***** _ _*****╚═╩══╩═╩═╩═╩╝╚╩═╩═╝***** _ ●▬▬▬▬▬▬▬๑۩۩๑▬▬▬▬▬▬▬▬●
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
Views: 1563 Team Technology
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/ Follow us on Facebook https://www.facebook.com/AllTech-1089946481026048/
Views: 32 AllTech
Views: 112 Noah Wang
Views: 180102 edureka!
Views: 149838 Siraj Raval
Views: 1227 Team Technology
Views: 301 Lazy Programmer
Views: 192 Lazy Programmer
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: 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. 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. ✅Subscribe: https://www.youtube.com/channel/UCub4qT8Sgm7ytZsO-jLL4Ow?sub_confirmation=1 📺Channel: https://www.youtube.com/channel/UCub4qT8Sgm7ytZsO-jLL4Ow? ▶️Watch Latest Python Content: https://www.youtube.com/watch?v=myCPgAO9BgQ&list=PLL3Qv26_SCsGWTF5PRaWUY0yhURFvco7L ▶️Watch Latest Other Content: https://www.youtube.com/watch?v=2YfQsLd2Ups&list=PLL3Qv26_SCsFVXXdsxOSB00RSByLSJICj&index=1 🐦Follow Rylan on Twitter: https://twitter.com/rylanpfowers The creator studies Applied and Computational Mathematics at BYU (BYU ACME or BYU Applied Math) and does work for the BYU Chemical Engineering Department 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