Introduction Ranking with MCDM You can’t rest on your #1 ranking-because the guy at #2 isn’t resting. He’s still improving…

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## Convex function of diagonals and eigenvalues

Sam Walters posted an elegant theorem on his Twitter account this morning. The theorem follows the pattern of an equality…

Continue Reading## Everything you Should Know about Confusion Matrix for Machine Learning

Confusion Matrix – Not So Confusing! Have you been in a situation where you expected your machine learning model to…

Continue Reading## An application of Kronecker products

A while back I wrote about Kronecker products in the context of higher order Taylor series. Here’s how I described…

Continue Reading## Understanding Class Sensitivity in Classification Problems (using a CIFAR-10 Case Study!)

Overview A fascinating deep dive into the problem of how many classes should you use in a classification problem The…

Continue Reading## Illustrating Cayley-Hamilton with Python

If you take a square matrix M, subtract x from the elements on the diagonal, and take the determinant, you…

Continue Reading## Master Dimensionality Reduction with these 5 Must-Know Applications of Singular Value Decomposition (SVD) in Data Science

3 Ways to Perform SVD in Python Applications of Singular Value Decomposition (SVD) We are going to follow a…

Continue Reading## Attention in Neural Networks

Let’s look at another example, “Post photos in your Dropbox folder to Instagram”. Compared to the previous one, here “Instagram”…

Continue Reading## Linear Algebra. Points matching with SVD in 3D space

Linear Algebra. Points matching with SVD in 3D spaceAndrey NikishaevBlockedUnblockFollowFollowingJun 30ProblemWe need to find best rotation & translation params between two…

Continue Reading## Recommendation Systems using UV-Decomposition

Maybe if we had additional features other than just movie series there might be some other similarities between the Star…

Continue Reading## An introduction to SVD and its widely used applications

An introduction to SVD and its widely used applicationsNathan ToubianaBlockedUnblockFollowFollowingJun 1If you’re in the data science world (or close to…

Continue Reading## Annotated Heatmaps in 5 Simple Steps

Annotated Heatmaps in 5 Simple StepsJulia KhoBlockedUnblockFollowFollowingMay 31A heatmap is a graphical representation of data in which data values are represented…

Continue Reading## Introduction to Latent Matrix Factorization Recommender Systems

Introduction to Latent Matrix Factorization Recommender SystemsTumas RackaitisBlockedUnblockFollowFollowingMay 30Latent Factors are “Hidden Factors” unseen in the data set. Lets use…

Continue Reading## Multi-Dimension Scaling

Dimension ReductionMulti-Dimension Scalingin PythonDiogo RibeiroBlockedUnblockFollowFollowingMay 29Photo by Lukasz Szmigiel on UnsplashMulti-Dimension Scaling is a distance-preserving manifold learning method. All manifold learning…

Continue Reading## Linear Classifiers: An Overview

Linear Classifiers: An OverviewThis article discusses the mathematical properties and practical Python applications of four popular linear classification methods. Michał OleszakBlockedUnblockFollowFollowingMay…

Continue Reading## Matrix in Python-Part1

Matrix in Python-Part1trainer. cppBlockedUnblockFollowFollowingMay 17In this story and next few parts we will see what is matrix both mathematically and…

Continue Reading## Practical Introduction to Hartree-Fock

Practical Introduction to Hartree-FockLaksh AithaniBlockedUnblockFollowFollowingMay 7We will write a Hartree-Fock algorithm completely from scratch in Python and use it to…

Continue Reading## An overview of Principal Component Analysis

How will it look?Variance is the expectation of the squared deviation of a random variable from its mean. Informally, it…

Continue Reading## Singular Value Decomposition vs. Matrix Factoring in Recommender Systems

I had to dig a little bit, but eventually, I found some hidden gems. According to Luis Argerich:The matrix factorization…

Continue Reading## Principal Component Analysis — Math and Intuition (Post 3)

I hope you chose the black line as it is closer to most of the data points (see figure below).…

Continue Reading## Principal Component Analysis — Math and Intuition (Post 2)

Principal Component Analysis — Math and Intuition (Post 2)Shivangi PatelBlockedUnblockFollowFollowingApr 4This is Post 2 of a 3-part series on Principal Component Analysis — Math and…

Continue Reading## Introduction to Convolutional Neural Networks (CNN) with TensorFlow

Introduction to Convolutional Neural Networks (CNN) with TensorFlowLearn the foundations of convolutional neural networks for computer vision and build a…

Continue Reading## Building a Music Recommendation Engine with Probabilistic Matrix Factorization in PyTorch

However, it should be noted that each of these matrices are randomly initialized. Therefore, in order for these predictions and…

Continue Reading## Random forest text classification in R: Trump v. Obama

Random forest text classification in R: Trump v. ObamaCan I successfully determine the differences in speech content between the 2…

Continue Reading## PCA and SVD explained with numpy

PCA and SVD explained with numpyZichen WangBlockedUnblockFollowFollowingMar 16How exactly are principal component analysis and singular value decomposition related and how to…

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