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  • OpenAIのBatch APIを使ってお得にプロンプトを一括処理してみる - Taste of Tech Topics

    はじめに こんにちは。データサイエンスチームYAMALEXのSsk1029Takashiです。 最近はOpenAIに日本支社が出来て、日本語対応が加速するというニュースにわくわくしています。 今回はそんなOpenAIから発表されたBatch APIという機能が便利、かつお得な機能だったのでどのように使えるのか試してみます。 Introducing the Batch API: save costs and get higher rate limits on async tasks (such as summarization, translation, and image classification). Just upload a file of bulk requests, receive results within 24 hours, and get 50% off API pri

      OpenAIのBatch APIを使ってお得にプロンプトを一括処理してみる - Taste of Tech Topics
    • Rustで扱える機械学習関連のクレート2021 - Stimulator

      - 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

        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

        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

            What We Learned from a Year of Building with LLMs (Part I)
          • 100+ Best GitHub Repositories For Machine Learning

            There are millions of GitHub repos and filtering them is an insane amount of work. It takes a huge time, effort, and a lot more. We have done this for you. In this article, we’ll share a curated list of 100+ widely-known, recommended, and most popular repositories and open source GitHub projects for Machine Learning and Deep Learning. So without further ado, Let’s see all the hubs created by exper

              100+ Best GitHub Repositories For Machine Learning
            • xvw.lol - Why I chose OCaml as my primary language

              This article is a translation, the original version is available here. I started using the OCaml language regularly around 2012, and since then, my interest and enthusiasm for this language have only grown. It has become my preferred choice for almost all my personal projects, and it has also influenced my professional choices. Since 2014, I have been actively participating in public conferences d

              • Why We Use Julia, 10 Years Later

                Exactly ten years ago today, we published "Why We Created Julia", introducing the Julia project to the world. At this point, we have moved well past the ambitious goals set out in the original blog post. Julia is now used by hundreds of thousands of people. It is taught at hundreds of universities and entire companies are being formed that build their software stacks on Julia. From personalized me

                  Why We Use Julia, 10 Years Later
                • The Pitchfork Story

                  A bit more than two years ago, as part of my work in Shopify’s Ruby and Rails Infrastructure team, I released a new Ruby HTTP server called Pitchfork. It has a bit of an unusual design and makes hard tradeoffs, so I’d like to explain the thought process behind these decisions and how I see the future of that project. Unicorn’s Design Is Fine Ever since I joined Shopify over 11 years ago, the main

                  • Accelerating Generative AI with PyTorch: Segment Anything, Fast – PyTorch

                    Blog Accelerating Generative AI with PyTorch: Segment Anything, Fast This post is the first part of a multi-series blog focused on how to accelerate generative AI models with pure, native PyTorch. We are excited to share a breadth of newly released PyTorch performance features alongside practical examples of how these features can be combined to see how far we can push PyTorch native performance.

                      Accelerating Generative AI with PyTorch: Segment Anything, Fast – PyTorch
                    • DoubleML — DoubleML documentation

                      Double Machine Learning Algorithm# Main Features# Double / debiased machine learning Chernozhukov et al. (2018) for Partially linear regression models (PLR) Partially linear IV regression models (PLIV) Interactive regression models (IRM) Interactive IV regression models (IIVM) The object-oriented implementation of DoubleML is very flexible. The model classes DoubleMLPLR, DoubleMLPLIV, DoubleMLIRM

                      • Simon Peyton Jones

                        Recorded 2022-02-01. Published 2022-03-25. Simon Peyton Jones is interviewed by Andres Löh and Joachim Breitner. Simon is the creator of Haskell and in this episode he talks about his new position at Epic, the origins of Haskell and why “it feels right”, and the (extra)ordinary Haskell programmers. Andres Löh: Hello Simon. Thank you so much for joining us today. Simon Peyton Jones: Hi Andres, hi J

                        • Nx (Numerical Elixir) is now publicly available - Dashbit Blog

                          Sean Moriarity and I are glad to announce that the project we have been working on for the last 3 months, Nx, is finally publicly available on GitHub. Our goal with Nx is to provide the foundation for Numerical Elixir. In this blog post, I am going to outline the work we have done so far, some of the design decisions, and what we are planning to explore next. If you are looking for other resources

                          • Mathematical Methods in Data Science (with Python)

                            Description This textbook on the mathematics of data has two intended audiences: For students majoring in math (or other quantitative fields like physics, economics, engineering, etc.): it is meant as an invitation to data science and AI from a rigorous mathematical perspective. For (mathematically-inclined) students in data science related fields (at the undergraduate or graduate level): it can s

                            • 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

                                Python/STAN Implementation of Multiplicative Marketing Mix Model, with Deep Dive into Adstock... | Towards Data Science
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