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Rohan Kotwani
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  • New Orleans, Louisiana
  • United States
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Rohan Kotwani posted a blog post

Create Transformed, N-Dimensional Polygons with Covariance Matrix

The covariance matrix has many interesting properties, and it can be found in mixture models, component analysis, Kalman filters, and more. Developing an intuition for how the covariance matrix operates is useful in understanding its practical implications. This article will focus on a few important properties, associated proofs, and then some interesting practical applications, i.e., extracting transformed polygons from a Gaussian mixture's covariance matrix.I have often found that research…See More
May 30
Rohan Kotwani posted a blog post

Create Transformed, N-Dimensional Polygons with Covariance Matrix

The covariance matrix has many interesting properties, and it can be found in mixture models, component analysis, Kalman filters, and more. Developing an intuition for how the covariance matrix operates is useful in understanding its practical implications. This article will focus on a few important properties, associated proofs, and then some interesting practical applications, i.e., extracting transformed polygons from a Gaussian mixture's covariance matrix.I have often found that research…See More
May 26
Rohan Kotwani's blog post was featured

Create Transformed, N-Dimensional Polygons with Covariance Matrix

The covariance matrix has many interesting properties, and it can be found in mixture models, component analysis, Kalman filters, and more. Developing an intuition for how the covariance matrix operates is useful in understanding its practical implications. This article will focus on a few important properties, associated proofs, and then some interesting practical applications, i.e., extracting transformed polygons from a Gaussian mixture's covariance matrix.I have often found that research…See More
May 26
Rohan Kotwani commented on Vincent Granville's blog post New Perspectives on Statistical Distributions and Deep Learning
"Very interesting! It makes sense that the mixture models is unstable and better for fitting an arbitrary distribution. I think having a weight sum of distributions is practical for when the underlying distribution have many anomalous events. My…"
Mar 3
Rohan Kotwani posted a blog post

High Density Region Estimation with KernelML

Data scientists and predictive modelers often use 1-D and 2-D aggregate statistics for exploratory analysis, data cleaning, and feature creation. Higher dimensional aggregations, i.e., 3 dimensional and above, are more difficult to visualize and understand. High density regions are one example of these N-dimensional statistics. High density regions can be useful for summarizing common characteristics across multiple variables. Another use case is to validate a forecast prediction’s plausibility…See More
Jan 6
Rohan Kotwani commented on Vincent Granville's blog post Free Book: Applied Stochastic Processes
"This book does a great job explaining concepts which are intuitive to data scientist/machine learning engineering in explainable language."
Jul 21, 2018
Gerardo Rojas liked Rohan Kotwani's blog post Generalized Machine Learning - Kerneml - Simple ML to train Complex ML
May 13, 2018
Rohan Kotwani commented on Rohan Kotwani's blog post Generalized Machine Learning - Kerneml - Simple ML to train Complex ML
"There is probably around a 70% chance getting a of boiled down (all the same stuff currently in kernelml) CRAN package within the next few weeks. Thanks for giving me the idea :) I'm also interested in testing the following: Multiprocessing -…"
May 12, 2018
Blaine Bateman liked Rohan Kotwani's blog post Generalized Machine Learning - Kerneml - Simple ML to train Complex ML
May 10, 2018
Blaine Bateman commented on Rohan Kotwani's blog post Generalized Machine Learning - Kerneml - Simple ML to train Complex ML
"This looks interesting. Any chance to implement in R?"
May 10, 2018
Rohan Kotwani posted blog posts
May 6, 2018
Rohan Kotwani's blog post was featured

Generalized Machine Learning - Kerneml - Simple ML to train Complex ML

I recently created a ‘particle optimizer’ and published a pip python package called kernelml. The motivation for making this algorithm was to give analysts and data scientists a generalized machine learning algorithm for complex loss functions and non-linear coefficients. The optimizer uses a combination of simple machine learning and probabilistic simulations to search for optimal parameters using a loss function, input and output matrices, and (optionally) a random sampler. I am currently…See More
May 4, 2018
Rohan Kotwani posted blog posts
May 4, 2018
Rohan Kotwani commented on Kostas Hatalis's blog post Swarm Optimization: Goodbye Gradients
Apr 25, 2018
Rohan Kotwani updated their profile
Apr 10, 2018

Profile Information

Short Bio
Interested in camera automation, time series forecasting, kernel methods, and sequential models.
My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/rkotwani
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Other
Your Job Title:
Data Scentist
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Contributing

Rohan Kotwani's Blog

Create Transformed, N-Dimensional Polygons with Covariance Matrix

Posted on May 26, 2019 at 7:30am 0 Comments

The covariance matrix has many interesting properties, and it can be found in mixture models, component analysis, Kalman filters, and more. Developing an intuition for how the covariance matrix operates is useful in understanding its practical implications. This article will focus on a few important properties, associated proofs, and then some interesting practical applications, i.e., extracting transformed polygons from a Gaussian mixture's covariance matrix.

I have often found that…

Continue

High Density Region Estimation with KernelML

Posted on January 3, 2019 at 4:00pm 0 Comments

Data scientists and predictive modelers often use 1-D and 2-D aggregate statistics for exploratory analysis, data cleaning, and feature creation. Higher dimensional aggregations, i.e., 3 dimensional and above, are more difficult to visualize and understand. High density regions are one example of these N-dimensional statistics. High density regions can be useful for summarizing common characteristics across multiple variables. Another use case is to validate a forecast prediction’s…

Continue

Generalized Machine Learning - Kerneml - Simple ML to train Complex ML

Posted on May 4, 2018 at 10:00am 2 Comments

I recently created a ‘particle optimizer’ and published a pip python package called kernelml. The motivation for making this algorithm was to give analysts and data scientists a generalized machine learning algorithm for complex loss functions and non-linear coefficients. The optimizer uses a combination of simple machine learning and probabilistic simulations to search for optimal parameters using a loss function, input and output matrices, and (optionally) a random…

Continue

How signal processing can be used to identify patterns in complex time series

Posted on January 18, 2017 at 6:30pm 3 Comments

The trend and seasonality can be accounted for in a linear model by including sinusoidal components with a given frequency. However, finding the appropriate frequency…

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