Joint, Conditional, and Marginal Probability Distributions
Joint probability, conditional probability, and marginal probability… These are three central terms when learning about probability, and they show up in Bayesian statistics as well. However… I never really could remember what they were, especially since we were usually taught them using formulas, rather than pictures. Well, for those... Read more
Deep Learning Question-Answer Model with Demo
  How amazing would be a model that can answer questions from any paragraph by extracting word(s) from the paragraph that are most relevant. Deep learning has made this possible! See below a demo for such a question-answer super model. In this demo you can put in any context up to 300 words... Read more
The Cold Start Problem
How do you operate a data-driven application before you have any data? This is known as the cold start problem. We faced this problem all the time when I designed clinical trials at MD Anderson Cancer Center. We uses Bayesian methods to design adaptive clinical trial designs, such as clinical trials... Read more
Analyze a Soccer Game Using Tensorflow Object Detection and OpenCV
Introduction The world cup season is here and off to an interesting start. Who ever thought the reining champions Germany would be eliminated in the group stage 🙁 For the data scientist within you lets use this opportunity to do some analysis on soccer clips. With the use of deep... Read more
Distribution of Eigenvalues for Symmetric Gaussian Matrix
Symmetric Gaussian matrices The previous post looked at the distribution of eigenvalues for very general random matrices. In this post we will look at the eigenvalues of matrices with more structure. Fill an n by n matrix A with values drawn from a standard normal distribution and let Mbe the average of A and its transpose, i.e. M = ½(A + AT).  The eigenvalues... Read more
A Research-Oriented Look at the Evolution of Word Embedding: Part I
Introduction “You shall know a word by the company it keeps,” insisted John R. Firth, a British linguist who performed pioneering work on collocational theories of semantics. What Firth meant by his 1957 quote was that interrogating the context in which a word is found offers clues to the... Read more
Mastering the Mystical Art of Model Deployment
With all the talk about algorithm selection, hyper parameter optimization and so on, you could think that training models is the hardest part of the Machine Learning process. However, in my experience, the really tricky step is to deploy these models safely in a web production environment. In this... Read more
Why are Convnets Often Better Than the Rest? Part II
Following on Part I of “Why are Convnets often Better than the Rest?”, we will now look at a traditional neural network’s weakness. As before, I am going to assume you understand the basics of neural network modeling already, including how images are applied to input layers. Through the... Read more
Mapping the Shifting Constellations of Online Debate
Online conversations, specially around contentious topics, are complex and dynamic. Mapping them is not just a matter of gathering enough data and applying sophisticated algorithms. It’s critical to adjust the map to the questions you want to answer; like models in general, no map is true, but some are... Read more
Bayesian Estimation, Group Comparison, and Workflow
Over the past year, having learned about Bayesian inference methods, I finally see how estimation, group comparison, and model checking build upon each other into this really elegant framework for data analysis. Parameter Estimation The foundation of this is “estimating a parameter”. In a typical situation, we are most... Read more
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