How clean are the data?Those are some interesting ideas to think about, also they have developed a level of data…

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## Credit Spread In Finance And Their Probability Distributions In Data Science

Credit Spread In Finance And Their Probability Distributions In Data ScienceUsing Python To Demonstrate Financial Credit Spreads And Hazard RatesFarhad MalikBlockedUnblockFollowFollowingJun 11One…

Continue Reading## After raw stats: exploring possession styles with data embeddings.

To keep it simple: it’s a flow of passes and moves until the team having the ball lose it. So…

Continue Reading## PCA Factors most sensitive to distributional changes

PCA Factors most sensitive to distributional changesVivek PalaniappanBlockedUnblockFollowFollowingJun 5This article is a summary and exploration of the research paper “Which…

Continue Reading## MCMC Intuition for Everyone

Can you think of a way?Think…. MCMC provides us with ways to sample from any probability distribution. Why would you…

Continue Reading## Behind the Models: Beta, Dirichlet, and GEM Distributions

Behind the Models: Beta, Dirichlet, and GEM DistributionsBuilding Blocks For Non-Parametric Bayesian ModelsTony PistilliBlockedUnblockFollowFollowingMay 31In a future post I want to…

Continue Reading## Teaching Neural Networks to Talk Like Painters Paint

Teaching Neural Networks to Talk Like Painters PaintJesus RodriguezBlockedUnblockFollowFollowingMay 28Conversational interfaces and natural language understanding(NLU) are one of the areas of…

Continue Reading## How to sample from language models

How to sample from language modelsBen MannBlockedUnblockFollowFollowingMay 24Causal language models like GPT-2 are trained to predict the probability of the next…

Continue Reading## The Central Limit Theorem and its Implications

The central limit theorem goes something like this, phrased statistics-encrypted:The sampling distribution of the sample means approaches a normal distribution…

Continue Reading## Hypothesis Testing Glossary for the Weary Reader

Hypothesis Testing Glossary for the Weary ReaderFrom “alpha” to “z-score”Steven RosaBlockedUnblockFollowFollowingJan 26TL;DR — Jump to glossaryWhy So Weary?When I try to read about statistics I…

Continue Reading## Getting started with Visualizations in Python

First things first, don’t even think of relating it with Bar graphs. -Histograms are very different from Bar graphs, in the…

Continue Reading## A stitch delayed — a modest fix for the biggest small problem in data science

(by which I mean if we just wrote down the two numbers — let’s say a mean of 1 kg and a…

Continue Reading## Explaining probability plots

SourceExplaining probability plotsIn this article I would like to explain the concept of probability plots — what they are, how to implement…

Continue Reading## Averages are Meaningless*

Using the average — the representative value. Count soldiers in a representative company, multiply by number of companies and you have the…

Continue Reading## Building a Content Based Recommender System for Hotels in Seattle

Photo Credit: PixabayBuilding a Content Based Recommender System for Hotels in SeattleHow to use description of a hotel to recommend similar hotels. Susan…

Continue Reading## Visualizing Beta Distribution and Bayesian Updating

As we flip the coin, we will observe a roughly equal number of heads and tails, and the more we…

Continue Reading## How to correctly select a sample from a huge dataset in machine learning

We must perform some kind of high-level comparison with the population made by the other editions. For example, we could…

Continue Reading## Overview of data distributions

Overview of data distributionsHow to choose the right distribution to model your dataMadalina CiortanBlockedUnblockFollowFollowingMar 10There are over 20 different types of…

Continue Reading## Probabilistic Graphical Models: Bayesian Networks

The venue, cuisine, distance from home, pricing etc. In general, we can write a custom program to answer our query…

Continue Reading## A brief introduction to Markov chains

A brief introduction to Markov chainsDefinitions, properties and PageRank example. Joseph RoccaBlockedUnblockFollowFollowingFeb 24This post was co-written with Baptiste Rocca. IntroductionIn 1998, Lawrence…

Continue Reading## Brief introduction to Markov chains

Brief introduction to Markov chainsDefinitions, properties and PageRank example. Joseph RoccaBlockedUnblockFollowFollowingFeb 24This post was co-written with Baptiste Rocca. IntroductionIn 1998, Lawrence Page,…

Continue Reading## Generating extinct Japanese script with Adversarial Autoencoders: Theory and Implementation

Generating extinct Japanese script with Adversarial Autoencoders: Theory and ImplementationAdrian Yijie XuBlockedUnblockFollowFollowingFeb 18IntroductionBe it political deepfakes, near real-time video modification,…

Continue Reading## A Hitchhiker’s Guide to Mixture Density Networks

Given a vector x of inputs (product attributes, customer, …, you name it again), we wish to predict y (price, website…

Continue Reading## Normal approximation to Laplace distribution?

Both distributions are symmetric about their means, so it’s natural to pick the means to be the same. So without…

Continue Reading## LDA Topic Modeling: An Explanation

LDA Topic Modeling: An ExplanationTyler DollBlockedUnblockFollowFollowingJun 24, 2018Photo by Patrick Tomasso on UnsplashBackgroundTopic modeling is the process of identifying topics in…

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