This is The Entire Computer Science Curriculum in 1000 YouTube Videos
This is The Entire Computer Science Curriculum in 1000 YouTube Videos In this article, we are going to create an entire Computer Science curriculum using only YouTube videos. The Computer Science curriculum is going to cover every skill essential for a Computer Science Engineer that has expertise in Artificial Intelligence and its subfields, like: Machine Learning, Deep Learning, Computer Vision,
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
- 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
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
Python open source libraries for scaling time series forecasting solutions
By Francesca Lazzeri. This article is an extract from the book Machine Learning for Time Series Forecasting with Python, also by Lazzeri, published by Wiley. In the first and second articles in this series, I showed how to perform feature engineering on time series data with Python and how to automate the Machine Learning lifecycle for time series forecasting. In this third and concluding article,
The Andrej Karpathy episode. Andrej explains why reinforcement learning is terrible (but everything else is much worse), why model collapse prevents LLMs from learning the way humans do, why AGI will just blend into the previous ~2.5 centuries of 2% GDP growth, why self driving took so long to crack, and what he sees as the future of education. Watch on YouTube; listen on Apple Podcasts or Spotify
訓練データ（教科書）での性能: R²スコアが0.551。これは、モデルが学習に使ったデータの内容を、約55%は説明できていることを示します。完璧ではありませんが、データから何らかのパターンを学習しようとした努力の跡が見えます。 テストデータ（本番試験）での性能: R²スコアが -0.205。これは衝撃的な結果です。マイナスの値は、モデルの予測が「常に全データの平均値を予測する」という最も単純な予測よりもさらに悪いことを意味します。つまり、このモデルは未知の問題に対しては全く役に立たない、むしろ有害でさえあるということです。 過学習の診断：グラフが語るモデルの「病状」 この「訓練データではそこそこ、テストデータでは最悪」という性能の大きなギャップこそが、「過学習」の典型的な症状です。モデルが訓練データに存在するパターンを「丸暗記」することに終始してしまい、物性の背後にある普遍的な法則を学ぶこ
GIMP 2.99.12 is a huge milestone towards GIMP 3.0. Many of the missing pieces are getting together, even though it is still a work in progress. As usual, issues are expected and in particular in this release which got important updates in major areas, such as canvas interaction code, scripts, but also theming… “CMYK space invasion”, by Jehan (based on GPLv3 code screencast), Creative Commons by-sa
NeurIPS 2022 参加報告 後編
はじめに プロダクトオーナー兼機械学習エンジニアの本田志温です。 弊社高橋による前回の記事「NeurIPS 2022 参加報告 前編」 に引き続き、同会議の参加報告をします。本記事では、個人的に気になった論文（計53本）をいくつかのカテゴリで分類し、カテゴリごとに研究トレンドを大づかみにできるような形で書きます。特に重要だと感じた論文は詳しめに取り上げます。 会場の様子 また、本記事に関心をお持ちになった方は以下の過去記事もお楽しみいただけるのではないかと思います。ぜひ合わせてご覧ください。 AI開発の新たなパラダイム「基盤モデル」とは NeurIPS 2021 参加報告 前編 NeurIPS 2021 参加報告 後編 深層学習の原理 深層学習は様々なタスクで高い性能を発揮することが経験的に知られていますが、「なぜうまくいくのか」という原理についてわかっていることは多くありません。そのため
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
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
17 types of similarity and dissimilarity measures used in data science. | Towards Data Science
The following article explains various methods for computing distances and showing their instances in our daily lives. Additionally, it… Various ML metrics. Inspired by Maarten Grootendorst. "There is no Royal Road to Geometry." – Euclid Quick note: Everything written and visualized has been created by the author unless it was specified. Illustrations and equations were generated using tools like
While getting my degree in Physics, I had to take classes in both MatLab and Python for scientific computing. I preferred python, where we used the SciPy and NumPy packages. In fact, I used those packages again (along with matplotlib) in an undergraduate research project simulating bacteria films. There's a catch: I was also pursuing a degree in Computer Science, and Python just wasn't fast enough
Pandas is a very popular tool for data analysis. It comes built-in with many useful features, it's battle tested and widely accepted. However, pandas is not always the best tool for the job. SQL databases have been around since the 1970s. Some of the smartest people in the world worked on making it easy to slice, dice, fetch and manipulate data quickly and efficiently. SQL databases have come such
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.
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
Faiss Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python. Some of the most useful algorithms are implemented on the GPU. It is
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
We have seen that Python language is a powerful tool for data science and data operations, but how powerful is Python for Data visualization? One of the key responsibilities of Data scientists is to communicate results effectively with the stakeholders. This is where the power of visualization comes into play. Creating effective visualizations helps businesses identify patterns and subsequently he
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
Goodbye, Data Science
This is more of a personal post than something intended to be profound. If you are looking for a point, you will not find one here. Frankly I am not even sure who the target audience is for this (probably “data scientists who hate themselves”?). I had been a data scientist for the past few years, but in 2022, I got a new job as a data engineer, and it’s been pretty good to me so far. I’m still wor
Version 1.0
Version 1.0# For a short description of the main highlights of the release, please refer to Release Highlights for scikit-learn 1.0. Legend for changelogs Major Feature something big that you couldn’t do before. Feature something that you couldn’t do before. Efficiency an existing feature now may not require as much computation or memory. Enhancement a miscellaneous minor improvement. Fix somethin
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
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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
100+ Best GitHub Repositories For Machine Learning
Andrej Karpathy — AGI is still a decade away
マテリアルズインフォマティクス（MI）入門①【線形回帰で挑む物性予測と「過学習」の罠】 - LabCode
GIMP - Development version: GIMP 2.99.12 Released
xvw.lol - Why I chose OCaml as my primary language
Why We Use Julia, 10 Years Later
Scientific Computing in Rust - aftix's dominion
Practical SQL for Data Analysis
DoubleML — DoubleML documentation
Welcome to Faiss Documentation — Faiss documentation
Data Visualization Using Python
Nx (Numerical Elixir) is now publicly available - Dashbit Blog