Lisp Advent Calendar 2016参加記事 ここ数年ディープラーニングの出現をきっかけにAIが再び盛り上がっているので、いよいよLispの復権があるかと思いきや、ないので(泣)、多少なりともLispに興味を持ってもらえるように、LispとAIの関係について私見を述べておこうと思う。Lispといっても色々あるが、この記事では主にCommon Lispの話になる。 Lispというとどうしても過去の記号処理的AIと結びつけられてしまい、機械学習を駆使するような現代のAIでは役に立たないように思われがちなのだが、これは大体誤解である。少なくともCommon Lispは現代的なAI開発に適した特徴を備えている。まず、AI実装のためのプログラミング言語に必要とされる特徴は何なのかを明らかにするために、AIの歴史から考えてみたい。 AIの歴史 初期の記号処理的AI(以降は記号AIと呼ぶ)
The International Conference on Machine Learning (ICML) and Computer Vision and Pattern Recognition (CVPR) 2016 occurred back-to-back this year. In this post, I’ll cover some of the most exciting advances in research as experienced from an on-the-ground perspective. Unsurprisingly, ICML focused more on fundamental research in an intimate setting while CVPR focused on applications research. Both in
The Top Five Languages Paving The Future Of Machine Learning by Mauricio Prinzlau The future of machine learning seems to be very bright, with leaping advances in software & technology and the proliferation of the cloud. It is currently one of the fastest emerging technologies in the world, with many experts claiming that it holds the key to unlocking the doors to computing’s most mystical evoluti
Introduction I like large-scale machine learning and love to build scalable learning systems. XGBoost is one such project that we created. It is the package you want to use to solve your data-science problems. I might be a bit exaggerate, however, XGBoost indeed has been used by a series of kaggle winning solutions as well as KDDCup winners. It is fast and optimized for out-of-core computations. Y
This post is aimed at readers who are familiar with Rust and want some insight into why it is great for machine learning. A machine learning problem Why is machine learning hard? What is rusty-machine? Where are we now? What’s next? A brief highlight of other community efforts I will pick a particular problem that can be solved using machine learning and talk (briefly) through one solution. This i
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