[WIP]Rustで「ゼロからつくるDeep Learning」 - Qiita
Deep Learningを学ぶべくゼロから作るDeep Learning ―Pythonで学ぶディープラーニングの理論と実装を読んだ。理解を深めるのとRustの学習を兼ねて、本書に書かれた内容をRustに書いてみる。なお筆者はDeep LearningにもRustにも、行列にも精通していない。 p26 2.3.2 重みとバイアスの導入 パーセプトロンのANDゲートを実装するのに、行列の計算に置き換える箇所。 行列の内積後、バイアスを加える処理をするところ。 行列の計算にはnagebraを使うことにした。 # [test] fn page_26() { let x = Matrix1x2::new(0.0, 1.0); println!("{}", x); let w = Vector2::new(0.5, 0.5); println!("{}", w); let b = Vector1:
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Deep Learning in Rust: a walk in the park
This is a sequel to my previous post several months ago. Last time, I introduced a shoddy library named deeplearn-rs that allowed you to build and (manually) train neural networks that run on your GPU via OpenCL. I told you to “take a gander” at some atrocious code that constructed a rather useless XOR network and then didn’t bother to train it. For reasons unknown, I still got positive feedback.
Machine Learning in Rust
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
TaPLのML実装をRustでやってみるシリーズ「7章 ラムダ計算のML実装」 - Qiita
// http://www.cis.upenn.edu/~bcpierce/tapl/checkers/untyped/ # ![allow(dead_code)] # ![allow(unused_variables)] # ![allow(unused_imports)] use std::fmt::Debug; use std::fmt::Formatter; use std::fmt::Error; # [derive(Clone,PartialEq)] pub enum Term { // Single Varialbe Var(isize, // de Bruijn Index. usize), // length of Context where this variable appealed. // Abstraction Abst(String, // lambda var
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