This is a living document. Everything in this document is made in good faith of being accurate, but like I just said; we don't yet know everything about what's going on.
Update: I've disabled comments as of 2025-01-26 to avoid everyone having notifications for something a year on if someone wants to suggest a correction. Folks are free to email to suggest corrections still, of course.
| {-# LANGUAGE TypeSynonymInstances #-} | |
| data Dual d = D Float d deriving Show | |
| type Float' = Float | |
| diff :: (Dual Float' -> Dual Float') -> Float -> Float' | |
| diff f x = y' | |
| where D y y' = f (D x 1) | |
| class VectorSpace v where | |
| zero :: v |
| #!/usr/bin/python3 | |
| # | |
| # Copyright 2020 Frank David Martinez M. (mnesarco at github) | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # |
site: https://tamuhey.github.io/tokenizations/
Natural Language Processing (NLP) has made great progress in recent years because of neural networks, which allows us to solve various tasks with end-to-end architecture. However, many NLP systems still require language-specific pre- and post-processing, especially in tokenizations. In this article, I describe an algorithm that simplifies calculating correspondence between tokens (e.g. BERT vs. spaCy), one such process. And I introduce Python and Rust libraries that implement this algorithm. Here are the library and the demo site links:
| sudo apt-get update | |
| sudo apt-get install libjemalloc-dev | |
| RUBY_CONFIGURE_OPTS='--with-jemalloc' rbenv install 2.6.3 | |
| # test (look for jemalloc warnings) | |
| MALLOC_CONF=invalid_flag:foo ruby -v |
| sda5_crypt UUID=c66880c1-c2f1-40fc-9580-f25d493876ef none luks,discard |
This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).
Matrix multiplication is a mathematical operation that defines the product of