Search results “Numpy vector product”
16 Numpy and linear algebra (AE1205 Python)
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
The Cross Product
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
NumPy Tutorials : 007 : Vector and Matrix Operations
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: 2392 Fluidic Colours
Inner & outer products
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
The difference between the dot product, and the inner product.
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
Sparse Matrices - Intro to Parallel Programming
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
Numpy Tutorial 6 Handlers Normal and Cross Products
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
3  Numpy Dot Product
For a complete course on machine learning do visit https://www.udemy.com/demystifying-ma... For a limited time, it is free
Vector Operations in Python
Here is a quick intro to vector calculations using VPython. https://trinket.io/glowscript/36bf2d2e8b
Views: 6855 Rhett Allain
NumPy Linear Algebra - Dr. Ahmad Bazzi
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: 11721 Ahmad Bazzi
Vector Norms
Views: 85411 ritvikmath
NumPy Tutorial 4(Transpose, Dot Multiplication, Vstack, Hstack, Flatten and Masking)
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
Python NumPy | Dot Product
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
Coding For Physics Majors: Dot Products In 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: 6430 Andrew Dotson
Matrix Operations in Python - How to Use Numpy Matrices
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: 12708 Zenva
Vectors - The Math of Intelligence #3
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 Vishnu's Winning Code: https://github.com/Sri-Vishnu-Kumar-K/MathOfIntelligence/blob/master/second_order_optimization_newtons_method/second_order_optimization.py Hammad's Runner-up Code: https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/blob/master/Newtons%20Method.ipynb Please Subscribe! And like. And comment. That's what keeps me going. 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 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ 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: 90471 Siraj Raval
Matrices and Vectors with Python | Create Row Vector, Column Vector | Calculate Dot Product - P9
''' 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)
Vectors and Matrices
Deep Learning Prerequisites: The Numpy Stack in Python https://deeplearningcourses.com
Views: 702 Lazy Programmer
#31 Python Tutorial for Beginners | Working with Matrix in Python
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: 90069 Telusko
Tensor products
I discuss tensor products.
Views: 60708 Jim Fowler
Understanding Vectors - Practical Machine Learning Tutorial with Python p.21
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: 63441 sentdex
Python Numpy Scalar Array Operation
Learn how to do Scalar Array Operation in Numpy Python.
Views: 656 DevNami
3.3.7-Linear Algebra: Vector and Matrix Norms
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
Python Numpy Array
This Python Numpy Matrix 5 minute tutorial gives basics on Matrices, Arrays and basic operations on them.
Views: 12 Nook Tutorials
Numpy Tutorial 5 Introduction to Dot Product
Introduction to dot products. Using the dot product to find what side of an arbitrarily rotated plane we're on.
Views: 479 Rich Colburn
Element Wise Multiplication in Python Numpy
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! :)
Python NumPy Tutorial | NumPy Array | Python Tutorial For Beginners | Python Training | Edureka
( 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 Subscribe to our channel to get video updates. Hit the subscribe button above. #Python #Pythontutorial #Pythononlinetraining #Pythonforbeginners #PythonProgramming #PythonNumpy How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course 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 - - - - - - - - - - - - - - - - - - - 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). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 227419 edureka!
numpy array multiply values
Code to compute the product of an array. Like and share. It's FREE too :) Download source code at: https://drive.google.com/file/d/1PEnBaP_Ji0YacRLnN9TgkziW7aIDMnv6/ Follow us on Facebook https://www.facebook.com/AllTech-1089946481026048/
Views: 31 AllTech
Dot product 2: Speed comparison
Deep Learning Prerequisites: The Numpy Stack in Python https://deeplearningcourses.com
Views: 670 Lazy Programmer
Dot product of two arrays using Python
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
Element-Wise Multiplication and Division of Matrices
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: 18905 LearnChemE
Machine learning W1 14 Matrix Vector Multiplication
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
numpy matrix multiply values
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: 45 AllTech
Matrices and Vectors with Python | How to reshape a matrix - P7
''' 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))
Mathematics - PCA - Dot product
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: 752 intrigano
Linear Algebra - Cosine & dot product
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: 1714 intrigano
Matrices and Vectors with Python | How to find the Diagonal of a matrix - P6
''' 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())
Matrices and Vectors with Python | Create | Access | Delete - P1
''' 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)
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 Vectors with Python | Maximum and Minimum Values of a Matrix - P4
''' 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))