Machine Learning with 10 Data Points - Or an Intro to PyMC3

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You've heard of big data, but what about small data?

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I'm a core dev on PyMC. This is a great video. For newer watchers know that PyMC3 has been superseded by PyMC v5 and its got so many cool new things

ravink
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Dude your video is amazing. So my clarity and simplicity around complex topic.
I really like how you keep re explaining basic terms as you cover them because it really helps following through into more advanced areas

cameronwebb
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Wow I really hope you dive into pymc3, I always had difficulty on understanding programming the priors. You're the best! This video is great. Sending love from South Korea :)

brycedavis
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This tutorial really ties everything together. Thank you.

jiaqint
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SUPER good video, I have been practicing on jupyter notebooks for data science and it has taken time but these videos make me feel like im standing on the shoulders of giants. THANKS

xxshogunflames
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Amazing as always! Just enough to wet the appetite for more PyMC3 learning. It looks like I may be using this library really soon.

pgbpro
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You teach so well! Please keep making videos!

sunilmathew
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Very nice video, well-organized and neatly explained concepts. One thing I would like to see at the end is some discussion on how would you use the posterior distributions, given that the true values are not obvious to infer from those at all :)

matakos
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Amazing job! So clear & easy to understand

umamiplaygroundnyc
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More video like this on Bayesian approaches.

orjihvy
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Nicely done! Very clear, concise yet informative. One thing missing from intro tutorials like this one is a real world example, with a a bigger dataset and more variables. Can pymc3 run on gpus, or computer clusters, etc or should I look elsewhere (pyro, tensor flow.probability)? Just giving an idea for future videos! Keep up the good work

EdoardoMarcora
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I'm subscribing, your explanation was on point!

MeshRoun
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Great stuff. You've won a new subscriber.

jfndfiunskj
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Great video, very well explained!! I would love to see you 💪 on another equation and distribution, something just a little harder like
Ytrue = X1^3 - X2^2 - X3
Y = Ytrue + binomial error distribution
Or something weird like that lol
Thanks for another great lesson!

MrMoore
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In other words, the MC in PyMC3 is for Markov Chain eh?

FabulusIdiomas
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What's the advantage of Bayesian analysis compared to calculating confidence intervals with linear regression and bootstrap?

zsoltczinege
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What are the advantages of this approach over use of the confidence intervals calculated in a linear model (e.g. as output by statsmodels?)

ppybmjc
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Ur videos r awesome thanks for adding in theory!

renaspersonal
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Thanks a lot for this video and the corresponding codes in GitHub! May Allah bless you!!!

komuna
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2:45 question - would linear regression give you distribution too? consider the confidence intervals of the parameter estimates.

jiayiwu