ksachdeva

[ksachdeva @ksachdeva]

@docmilanfar For me first understanding SSM (the model) and then Bayesian view on filtering and seeing Kalman filter an “algo” for LGSSM (model) was useful. I explain it here - https://t.co/GxQOer4D39

[ksachdeva @ksachdeva]

@EikoFried @rlmcelreath Indeed, initially this single term created lot of challenges for me https://t.co/JsyRE9vE37

[ksachdeva @ksachdeva]

An in-depth explanation of challenges and one potential remedy for gradient estimation in stochastic computational graphs https://t.co/yUZeJ3pIXG

[ksachdeva @ksachdeva]

Variational Autoencoder, Latent Variable Model & Amortized Inference... https://t.co/J9KmlQRjwv

[ksachdeva @ksachdeva]

What is Probabilistic Programming? + A quick & opinionated review of @mcmc_stan @pymc_devs @PyroAi @TuringLang @TensorFlow Probability (TFP) https://t.co/FT88QWm5Le using an example from @rlmcelreath's Statistical Rethinking book

[ksachdeva @ksachdeva]

The Metropolis Algorithm. Watch the full tutorial (The Metropolis-Hastings Algorithm) on youtube (https://t.co/n2IQRuC5sg)

[ksachdeva @ksachdeva]

@BecomingDataSci A series of YouTube tutorials on Bayesian regression & inference. So far 9 videos and many more to come. https://t.co/MIZhV29juR . An attempt to cover both the intuition and underlying mathematics. 🙏

[ksachdeva @ksachdeva]

https://t.co/gbl6BdHCCs

[ksachdeva @ksachdeva]

@rlmcelreath .. students admire & love you back for all the wisdom you grant.

[ksachdeva @ksachdeva]

Published a tutorial on Importance Sampling https://t.co/ADhoxbsIzg

[ksachdeva @ksachdeva]

A visual introduction to Rejection Sampling! Full tutorial on YouTube https://t.co/uYddxLnPjO

[ksachdeva @ksachdeva]

Published a video explaining "MADE: Masked Autoencoder for Distribution Estimation" https://t.co/AR4wX6pSAX MADE is an essential component in many modern Normalizing Flows algorithms

[ksachdeva @ksachdeva]

Published a comprehensive tutorial on Normalizing Flows - Motivations, The Big Idea, & Essential Foundations https://t.co/u50qONqaw2

[ksachdeva @ksachdeva]

@jeremyphoward The complexity/Simplicity of a language is a function of how it is taught and the domain for which it is designed! E.g. Your fastai effort makes the complex subject of ML relatively simpler. So what is the problem - language or the teachers :)

[ksachdeva @ksachdeva]

@willkurt IMHO reason great libraries got written in python are because of domain expertise & not because language is superior. Refactoring/updates/performance/lack of tests are real issues in any industrial code & python makes my heart stop because of lack of strong typing and tools.

[ksachdeva @ksachdeva]

@smdiehl @willkurt If you take inspiration from JavaScript community then it can be done. Many JavaScript libraries did manage to have type definitions. It is about the community effort... seems most of the python developers still do not appreciate the importance of type annotations.

[Towards Data Science @TDataScience]

But what is Entropy? by @ksachdeva https://t.co/cZqVBCv9ik

[Peter Kolchinsky @PeterKolchinsky]

If you are hearing about #covid19 “reinfections” in Asia, I can offer you my take as a virologist. The best explanation for what we’re seeing is likely due to three things...

[ksachdeva @ksachdeva]

@ozataman @rlmcelreath @fehiepsi @junpenglao Yes there is one for pyro (1st Ed) and numpyro (2nd Ed) by @fehiepsi ..see the links to all the ports here - https://t.co/hOSjobUyC2

[ksachdeva @ksachdeva]

Statistical Rethinking (2nd Ed) Port to Tensorflow Probability https://t.co/Wiyjw9gJnK Thanks @rlmcelreath @fehiepsi @junpenglao