- はじめに - 本記事では、Rustで扱える機械学習関連クレートをまとめる。 普段Pythonで機械学習プロジェクトを遂行する人がRustに移行する事を想定して書くメモ書きになるが、もしかすると長らくRustでMLをやっていた人と視点の違いがあるかもしれない。 追記：2021/02/24 repositoryにしました。こちらを随時更新します github.com 追記；2021/07/26 GitHub Pagesでウェブサイトにしました vaaaaanquish.github.io - はじめに - - 全体感 - - 機械学習足回り関連のクレート - Jupyter Notebook Numpy/Scipy Pandas 画像処理 形態素解析/tokenize - scikit-learn的なやつ - 各ライブラリと特徴比較 - Gradient Boosting - XGBoos
January 30, 2023 In this post, we'll implement a GPT from scratch in just 60 lines of numpy. We'll then load the trained GPT-2 model weights released by OpenAI into our implementation and generate some text. Note: This post assumes familiarity with Python, NumPy, and some basic experience with neural networks. This implementation is for educational purposes, so it's missing lots of features/improv
What We Learned from a Year of Building with LLMs (Part I)
It’s an exciting time to build with large language models (LLMs). Over the past year, LLMs have become “good enough” for real-world applications. The pace of improvements in LLMs, coupled with a parade of demos on social media, will fuel an estimated $200B investment in AI by 2025. LLMs are also broadly accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into
Python/STAN Implementation of Multiplicative Marketing Mix Model, with Deep Dive into Adstock... | Towards Data Science
Python/STAN Implementation of Multiplicative Marketing Mix Model, with Deep Dive into Adstock… Python/STAN Implementation of Multiplicative Marketing Mix Model With Deep Dive into Adstock, Diminishing Return, ROAS, and mROAS Full code and simulated dataset are posted on my Github repo: https://github.com/sibylhe/mmm_stan The methodology of this project is based on this paper by Google, but is appl
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Rustで扱える機械学習関連のクレート2021 - Stimulator
GPT in 60 Lines of NumPy | Jay Mody