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: 980 Prof Hoekstra
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: 739066 patrickJMT
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: 500 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: 57733 sentdex
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
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: 7743 JJtheTutor
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: 82079 Siraj Raval
This covers the main geometric intuition behind the 2d and 3d cross products. *Note, in all the computations here, I list the coordinates of the vectors as columns of a matrix, but many textbooks put them in the rows of a matrix instead. It makes no difference for the result, since the determinant is unchanged after a transpose, but given how I've framed most of this series I think it is more intuitive to go with a column-centric approach. Full series: http://3b1b.co/eola Future series like this are funded by the community, through Patreon, where supporters get early access as the series is being produced. http://3b1b.co/support ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted about new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: https://goo.gl/WmnCQZ Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
Views: 395883 3Blue1Brown
Ayo Belajar NumPy dengan Python di seri Tutorial python data analisis. Perkalian Vektor dot dan cross ====================================== Source Code bisa didownload di: https://github.com/kelasterbuka ====================================== Playlist Python: Tutorial Basic Python : https://www.youtube.com/playlist?list=PLZS-MHyEIRo7cgStrKAMhgnOT66z2qKz1 Tutorial Python OOP : https://www.youtube.com/playlist?list=PLZS-MHyEIRo7ab0-EveSvf4CLdyOECMm0 enjoy gan, keep learning keep awesome!!!! ====================================== Follow kami di: https://www.instagram.com/kelasterbuka https://www.facebook.com/KelasTerbukaIndonesia https://www.twitter.com/kelasterbuka_id
Views: 643 Kelas Terbuka
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: 5619 MathTheBeautiful
Slicing, bool arrays, and logical indexing
Views: 956 Rich Colburn
** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. 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: 62365 edureka!
numpy's outer provides a straightforward way of applying a simple function across all pairs of two arrays of arbitrary dimension. Brian McFee covers two cases where outer allows for dramatic speed-ups when used in place of nested for loops
Views: 391 Crucial Python
Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! Area of Triangle Formed by Two Vectors using Cross Product. Here we find the area of a triangle formed by two vectors by finding the magnitude of the cross product.
Views: 131147 patrickJMT
In this video, I introduce Einstein notation (or Einstein Summation Convention), one of the most important topics in Tensor Calculus. Einstein notation is a way of expressing sums in short-form; repeated indices are used to denote the index that is summed over. I describe the 4 major rules of Einstein notation, as well as the definitions of free and dummy indices. I also discuss some important information related to these major rules. Questions/requests? Let me know in the comments! Prerequisites: The videos before this one on this playlist: https://www.youtube.com/playlist?list=PLdgVBOaXkb9D6zw47gsrtE5XqLeRPh27_ Lecture Notes: https://drive.google.com/open?id=1qgQvuoDU_1EScznBjWzHc_dV_GJGCsmU Patreon: https://www.patreon.com/user?u=4354534 Twitter: https://twitter.com/FacultyOfKhan Special thanks to my Patrons for supporting me at the $5 level or higher: - Jose Lockhart - James Mark Wilson - Yuan Gao - Marcin Maciejewski - Sabre - Jacob Soares - Yenyo Pal - Lisa Bouchard - Bernardo Marques - Connor Mooneyhan - Richard McNair
Views: 17302 Faculty of Khan
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: 374730 Welch Labs
Simon did the 2D, 3D and 4D classes but eventually got stuck with the matrix class in Python. He then opened his old Xamarin IDE and wrote the 2D, the 3D and the 4D classes in C#. In the video below he briefly shows his C# sketch and talks about Cross Product in general. For the Python videos see: https://www.youtube.com/watch?v=SIu7EpS8VPA https://www.youtube.com/watch?v=1X9sm1YbGJk
Views: 164 Simon Tiger
Deep Learning Prerequisites: The Numpy Stack in Python https://deeplearningcourses.com
Views: 754 Lazy Programmer
''' 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)
Views: 311 MachineLearning with Python
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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 149793 Siraj Raval
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: 706 Rylan Fowers
This video explains what is meant by the covariance and correlation between two random variables, providing some intuition for their respective mathematical formulations. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti
Views: 283872 Ben Lambert
Any advertising proceeds will be donated to the Department of Mathematics, Statistics and Computer Science at the University of Wisconsin-Stout.
Views: 78 Abraham Smith
The correlation coefficient is a really popular way of summarizing a scatter plot into a single number between -1 and 1. In this video, I'm giving an intuition how the correlation coefficient does this, without going into formulas. If you need to calculate the correlation coefficient for some data, you can find the formula here: https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient#For_a_sample This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.(http://creativecommons.org/licenses/by-nc/4.0/)
Views: 451671 Benedict K
To ask any doubt in Math download Doubtnut: https://goo.gl/s0kUoe Question: Find the area of parallelogram whose diagonals are the vectors 3 hat i + hat j - hat k and hat i - 3 hat j + 4 hat k.
Views: 748 Doubtnut
This is to finish out the first Python tutorial I created under Jupyter Notebook to create dictionary object that goes from spreadsheet-style references of column A through column ZZ.
Views: 1039 Mike Levin, SEO in NYC
matlab tutorial,complete matlab course,free udemy course,beginner,advanced,all levels,probability,image manipulation,signal processing,plotting data,data analysis,solving equations,linear algebra,how to get started,programming languages,engineering,science,economics
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: 1218 intrigano
PyData Ann Arbor Meetup - May 9, 2018 Sponsored by NumFOCUS and TD Ameritrade https://www.meetup.com/PyData-Ann-Arbor/ Travis Oliphant | XND: A Cross-language Set of Libraries for General Typed Containers NumPy is the foundation of array computing in Python. However, it has many known limitations including a difficult to extend type-system, and a function system that is difficult to extend to other types. Over the past decade, other Python run-times and other languages have tried to mimic NumPy behavior and more recently many machine learning frameworks have created more array concepts that are not necessarily compatible with each other. XND is a set of libraries and concepts that can unify the foundations of computing with typed containers across languages. It builds on the lessons of NumPy while generalizing it's core features into re-usable libraries. In this talk, I will describe the need for XND and discuss it's progress and roadmap. ----- Travis Oliphant has a PhD from the Mayo Clinic in Biomedical Engineering. He taught Inverse Problems and statistical signal processing at BYU for 7 years. He has spent the past 20 years in the Python Data and Science community. He was the principal author of SciPy, the creator of NumPy, initial leader of the Numba project, and Conda projects, and most recently the XND (Plures) project. He has a passion for organizing business activity to support open source communities and is the founder of Anaconda (Continuum Analytics), NumFOCUS, and most recently Quansight.
Views: 795 PyData
In the "Mathematical devices in deep learning II, matrix dot product is covered
Views: 520 Vasu Srinivasan
University of Hawaii, Dept. of Geology & Geophysics, Garrett Apuzen-Ito, GG413: Geological Data Analysis www.soest.hawaii.edu/GG/FACULTY/ITO/GG413
Views: 2300 Garrett Apuzen-Ito
I hadn't seen that matrix multiplies are really just dot products until last week.
Views: 4186 Hamilton Carter
Materials to follow along with the tutorial may be found at http://www.labri.fr/perso/nrougier/teaching/matplotlib/matplotlib.html and here github.com/rougier/matplotlib-tutorial After reviewing the main concepts for scientific figures creation (based on the "Ten simple rules for better figures" article), we will experience specifically the matplotlib library that provides many different types of high-quality figures with only a few lines of code. We'll go through the creation of a simple, but carefully crafted figure and see in the meantime the main concepts of the library. Then, we'll go through an animation example showing the last 50 earthquakes on the planet and we'll finish the tutorial with a set of exercises showing the main type of plots. Last, we'll have a look at available resources for advanced uses. See the complete SciPy 2016 Conference talk & tutorial playlist here: https://www.youtube.com/playlist?list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6
Views: 8644 Enthought
In the video, I am trying track multiple colors in this video the colors like RED, BLUE, orange and YELLOW. Using Python programming language and OpenCV 2.0 . The link the for another tutorial of color filtering is : https://www.youtube.com/watch?v=CCOXg... Changing Colorspaces http://opencv-python-tutroals.readthe... There is a simple program to get HSV Codes in realtime: http://stackoverflow.com/questions/10...
Views: 55 Sandeep Kumar
This is video number 3 in the Linear Algebra series by Free Academy
Views: 123 Free Academy for Math