Search results “Scalar product numpy”
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: 6916 JJtheTutor
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: 3615 Andrew Dotson
17. Numpy 2D Arrays
Views: 468 Cognitive Class
Python Numpy Scalar Array Operation
Learn how to do Scalar Array Operation in Numpy Python.
Views: 508 DevNami
Python Tutorial For Beginners Part 2: Numpy Matrix and Array Operations
Learn Matrix manipulations, Array, Scalar and Vector Operations, Using Loops for Matrix, Matrix Concatenation and some simple Numpy operations
Views: 4006 rupam rupam
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: 1068 IT Connected
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: 45 Shah Quadri
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: 9351 Zenva
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: 361 Rich Colburn
Linear Algebra 20j: The Dot Product, Matrix Multiplication, and the Magic of Orthogonal Matrices
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: 5314 MathTheBeautiful
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: 1266 Fluidic Colours
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: 797 Prof Hoekstra
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: 7608 Ahmad Bazzi
Matrix Multiplication in Python
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: 4694 AI Medicines
Numpy and Loops in Python
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: 5385 APMonitor.com
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
Views: 79379 Siraj Raval
The Dot Operator in Python and ArcPy
How to use the dot operator in ArcPy and Python.
Views: 2027 Richard Smith
Python numpy array operation tutorial-2
This video will teach different operation on array in numpy. Indexing Reshaping Max, min, argmax, argmin, sort +, - , *, /,Power Mean, std Cross, Dot Visit complete course on Data Science with Python : https://www.udemy.com/data-science-with-python-and-pandas/?couponCode=YTSOCIAL090 For All other visit my udemy profile at : https://www.udemy.com/user/ankitmistry/
Views: 898 MyStudy
Tensor products
I discuss tensor products.
Views: 56124 Jim Fowler
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: 478 Rich Colburn
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)
Dot product 1: For loop vs. cosine method vs. dot function
Deep Learning Prerequisites: The Numpy Stack in Python https://deeplearningcourses.com
Views: 668 Lazy Programmer
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: 730923 patrickJMT
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: 1558 Jeffrey Chasnov
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: 55560 sentdex
Numpy and Matplotlib Tutorial
#Numpy #Matplotlib #MachineLearning #DataAnalytics #DataScience This Tutorial is a part of the series Data Analytics with Python. This video is a tutorial to learning Numpy and Matplotlib in Python. What is Numpy used for ? Numpy arrays are very fast and efficient for mathematical operations. The ndarrays for Numpy add functionality for multi dimentional arrays. What is Matplotlib? Matplotlib is an extension for Numpy with the ability of plotting graphs and Data Visualization. The functions covered in this tutorial are: Numpy : - List to numpy array - Multiplication - np.arange (Generating numbers with specified gaps) - Multidimentional Array - ndim (checking the dimensions of array) - np.shape() - np.random.randn() - Accessing via Index Matplotlib: - pyplot - Adding labels - Changing scale of Axis - Different color and shape of plot points - Plot more than one graph For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Pattern Recognition and Machine Learning : http://amzn.to/2p6mD6R
Views: 6923 The Semicolon
#27 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
Views: 40736 Telusko
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: 1012 intrigano
9- NumPy: Array Transpose
Visit my personal web-page for the Python code: www.imperial.ac.uk/people/n.sadawi
Views: 5336 Noureddin Sadawi
4- NumPy Array Operations
Visit my personal web-page for the Python code: www.imperial.ac.uk/people/n.sadawi
Views: 4636 Noureddin Sadawi
Matrix Multiplication in Python
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: 3660 Pratik Matkar
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: 166149 edureka!
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: 25 AllTech
Python: Numpy Hstack
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: 533 Rylan Fowers
Vectors and Matrices
Deep Learning Prerequisites: The Numpy Stack in Python https://deeplearningcourses.com
Views: 524 Lazy Programmer
Support Vector Machines - The Math of Intelligence (Week 1)
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: 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
Views: 142228 Siraj Raval
numpy matrix multiply row values
Code to compute the product of all values for each row in a matrix. Like and share. It's FREE too :) Download source code at: https://drive.google.com/file/d/1LdRhhbrCXChQtJLYi53iRG9VQQr7njRQ/ Follow us on Facebook https://www.facebook.com/AllTech-1089946481026048/
Views: 14 AllTech