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You are here		blog.jle.im Practical Dependent Types in Haskell: Type-Safe Neural Networks (Part 1) · in Code
\|	\|	bartoszmilewski.com Neural Networks, Pre-Lenses, and Triple Tambara Modules \| Bartosz Milewski's Programming Cafe	10.9 parsecs away Travel
\|	\|	Introduction Neural networks are an example of composable systems, so it's no surprise that they can be modeled in category theory, which is the ultimate science of composition. Moreover, the categorical ideas behind neural networks can be immediately implemented and tested in a programming language. In this post I will present the Haskell implementation of...	10.9 parsecs away Travel
\|	\|	degoes.net A Glossary of Functional Programming - John A De Goes	9.7 parsecs away Travel
\|	\|	Functional programming has a bit of jargon, but that doesn't have to stop you from understanding core concepts	9.7 parsecs away Travel
\|	\|	danilafe.com Chapel's Runtime Types as an Interesting Alternative to Dependent Types	13.4 parsecs away Travel
\|	\|	In this post, I discuss Chapel's runtime types as a limited alternative to dependent types.	13.4 parsecs away Travel
\|	\|	fa.bianp.net How to Evaluate the Logistic Loss and not NaN trying	110.9 parsecs away Travel
\|		A naive implementation of the logistic regression loss can results in numerical indeterminacy even for moderate values. This post takes a closer look into the source of these instabilities and discusses more robust Python implementations. hljs.initHighlightingOnLoad(); MathJax.Hub.Config({ extensions: ["tex2jax.js"], jax: ["input/TeX", "output/HTML-CSS"], tex2jax: { inlineMath ...	110.9 parsecs away Travel