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4:12 No thing other than the posterior over models is more acceptable to God, and escheweth evil?
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30:23 Then said Elkanah her husband liveth; but only if the mode is a Gaussian prior.
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Carefully evaluating precise gradients using large datasets is often better than a strong hand, hath the LORD brought forth into their mind.
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47:13 And he said unto thy neighbour, do the gradient descent.
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Jesus of the Hebrews that were added to the LORD, he is gracious and merciful, slow to overfit, as is the value of λ.
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He said unto them, Have ye suffered so many equations?
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21:17 And David said to the Lord GOD; less data, is best.
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I present Love Thy Nearest Neighbor: a Markov chain generator trained on the King James Bible and Kevin Murphy’s Machine Learning: A Probabilistic Perspective. Behold...
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It took me 20 minutes to fall in love with
@RoamResearch#roamcultHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Shiri’s Scissor but for machine learning (https://slatestarcodex.com/2018/10/30/sort-by-controversial/ …)https://twitter.com/carlesgelada/status/1208618401729568768 …
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The answer depends on what kind of future data you wish to generalize to. If you want to generalize to new time points for a given patient, then you have tons of data! But if you want to generalize to new patients… then n=5. 2/2
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A deceptively difficult question: how much data do I have? If your data is hierarchical the answer is not obvious. Say I have a dataset of 5 patients, and for each one, fine-grained measurements across 1 million time points. Is my sample size 5 or 5 million? 1/2
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Miles Turpin proslijedio/la je Tweet
Just sent a Momentum gift card (http://www.giftmomentum.com ). A fun way to introduce friends/family to effective giving!
@GiveMomentum@willmacaskillpic.twitter.com/yjxBNZGo67
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Miles Turpin proslijedio/la je Tweet
We’ve made the decision to stop all political advertising on Twitter globally. We believe political message reach should be earned, not bought. Why? A few reasons…
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Miles Turpin proslijedio/la je Tweet
This abstractive summarization paper abstract is a real roller coaster! https://arxiv.org/abs/1909.03186 pic.twitter.com/MSKQ796Ulr
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Miles Turpin proslijedio/la je Tweet
Over the years, I've watched conditional renormalization grow from a style transfer hack to a key mechanism in recent
#MachineLearning results; in this post I chart out how the idea evolved from humble beginnings into a flexible and important techniquehttps://medium.com/@cody.marie.wild/conditional-love-the-rise-of-renormalization-techniques-for-neural-network-conditioning-14350cb10a34 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Miles Turpin proslijedio/la je Tweet
I made a Markov chain text generator and trained it on the concatenated text of the King James Bible and
@michael_nielsen & Chuang's canonical quantum information book (the quantum bible). Some of the finer pearls of wisdom I found:Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Miles Turpin proslijedio/la je Tweet
Progress towards InfiniGAN continues.
#Representhttps://twitter.com/poolio/status/1148108915294400512 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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