Transformer Explainer: LLM Transformer Model Visually Explained
What is a Transformer? Transformer is a neural network architecture that has fundamentally changed the approach to Artificial Intelligence. Transformer was first introduced in the seminal paper "Attention is All You Need" in 2017 and has since become the go-to architecture for deep learning models, powering text-generative models like OpenAI's GPT, Meta's Llama, and Google's Gemini. Beyond text, T
機械学習初心者がKaggleの「入門」を高速で終えるための、おすすめ資料などまとめ（2023年12月版）｜カレーちゃん
機械学習初心者がKaggleの「入門」を高速で終えるための、おすすめ資料などまとめ（2023年12月版） こんにちわ、カレーちゃんです。Kaggle GrandMasterです。 Kaggleはデータサイエンスに入門するのにとても適しています。ですが、英語の問題などがあり、入門するのが難しい。そこで、Kaggleの「入門」をこうすれば高速に完了できるというnoteを書きます。 同じタイトルの記事を、2020年8月にも書いたのですが、それから2年以上がたちました。それから、おすすめできる資料が増え、また、私が思う入門のコースもやや変わりましたので、更新をしたいと思います。 1.Kaggleに入門（はじめに取り組むと良い資料）Kaggleには、「タイタニックコンペ」という、練習用のコンペがあります。 これは、事故が起こったタイタニックの乗客のデータから、乗客の生死を予測するという、やりたいこと
State of JavaScript 2023
It should be clear by now that, for better or for worse, JavaScript is not slowing down. Between server components, server actions, signals, compilers, and more, we're seeing new innovations pop up faster than most of us can handle. The trick to avoiding the dreaded JavaScript fatigue is remembering that you can pick your lane: sure, you can live life on the cutting edge with the early adopters; b
Introducing postgres.new, the in-browser Postgres sandbox with AI assistance. With postgres.new, you can instantly spin up an unlimited number of Postgres databases that run directly in your browser (and soon, deploy them to S3). Each database is paired with a large language model (LLM) which opens the door to some interesting use cases: Drag-and-drop CSV import (generate table on the fly) Generat
State of HTML 2023
While JavaScript was taking over the web, and CSS was gaining new superpowers year over year, it could seem like HTML was content to stay dormant, happy to cede center stage to its younger siblings. After all once you've learned about <div>s and <h>s 1 through 6, what else is there to know? Quite a lot, as it turns out! Once again we drafted Lea Verou to put her in-depth knowledge of the web platf
良いグラフと悪いグラフの違いとは？
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Elasticsearch Query Language (ES|QL) now generally available — Search Labs
Elasticsearch piped query language, ES|QL, now generally available Today, we are pleased to announce the general availability of ES|QL (Elasticsearch Query Language), a dynamic language designed from the ground up to transform, enrich, and simplify data investigations. Powered by a new query engine, ES|QL delivers advanced search using simple and familiar query syntax with concurrent processing, e
Did Google Just Kill Streamlit?
Sitting on my couch, browsing through YouTube recommendations, I came across a video by Prompt Engineering on Google’s latest open-source Python framework named ‘Mesop,’ which is used by teams at Google for internal tooling and rapid prototyping. If you are wondering what ‘Mesop’ means, stop overthinking. Not everything in life makes sense. Will, if you are reading this, drop it in the comments be
Announcing Observable 2.0
Today we’re launching Observable 2.0 with a bold new vision: an open-source static site generator for building fast, beautiful data apps, dashboards, and reports. Our mission is to help teams communicate more effectively with data. Effective presentation of data is critical for deep insight, nuanced understanding, and informed decisions. Observable notebooks are great for ephemeral, ad hoc data ex
Look-up-tables, more commonly referred to as LUTs, are as old as Mathematics itself. The act of precalculating things into a row or table is nothing new. But in the realm of graphics programming, this simple act unlocks some incredibly creative techniques, which both artists and programmers found when faced with tough technical hurdles. We’ll embark on a small journey, which will take us from simp
はじめに
このドキュメントは『指標・特徴量の設計から始めるデータ可視化学入門』で提供されているPythonによる可視化コードをR言語で書き直したものです。 ただし、Pythonのコードの直訳・逐語訳ではなくRらしい書き方・表現へ意訳しています。 以下が各章ごとのドキュメントです。 2023年2月時点で第8章まで完成。 第1章 データ可視化の本質 第2章 数量を把握するデータ可視化 第3章 メカニズムをとらえるデータ可視化 第4章 多変数をとらえるデータ可視化 第5章 データの分布をとらえる指標化 第6章 関係性をとらえる指標化 第7章 パターンをとらえる指標化 第8章 データ指標化・可視化のプロセス MATLAB版も公開されています。 以下の方針を取っています。 書籍とPythonのコードで微妙に異なる箇所は、極力書籍に合わせる。 配色の再現は目指さない。 jetカラーのグラデーションはviridi
ITスキルロードマップ roadmap.sh がすごい。Data Analyst について対応する本をまとめた - Qiita
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State of JavaScript 2023
It should be clear by now that, for better or for worse, JavaScript is not slowing down. Between server components, server actions, signals, compilers, and more, we're seeing new innovations pop up faster than most of us can handle. The trick to avoiding the dreaded JavaScript fatigue is remembering that you can pick your lane: sure, you can live life on the cutting edge with the early adopters; b
Linux Foundation Announces OpenSearch Software Foundation to Foster Open Collaboration in Search and Analytics
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State of HTML 2023
While JavaScript was taking over the web, and CSS was gaining new superpowers year over year, it could seem like HTML was content to stay dormant, happy to cede center stage to its younger siblings. After all once you've learned about <div>s and <h>s 1 through 6, what else is there to know? Quite a lot, as it turns out! Once again we drafted Lea Verou to put her in-depth knowledge of the web platf
Octoverse: AI leads Python to top language as the number of global developers surges
Remember when people said AI would replace developers? Our data tells a different story. As AI rapidly expands, developers are increasingly building AI models into applications and engaging with AI projects on GitHub in large numbers. At the same time, we’re seeing an unprecedented number of developers join GitHub from across the globe, and many of these developers are contributing to open source
State of JavaScript 2023
It should be clear by now that, for better or for worse, JavaScript is not slowing down. Between server components, server actions, signals, compilers, and more, we're seeing new innovations pop up faster than most of us can handle. The trick to avoiding the dreaded JavaScript fatigue is remembering that you can pick your lane: sure, you can live life on the cutting edge with the early adopters; b
Agents for Amazon Bedrock now support memory retention and code interpretation (preview) | Amazon Web Services
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An easy look at Grafana architecture Delve into Grafana's architecture in this article, where I strive to simplify complex concepts. Explore the components that make up Grafana and gain a clear understanding of how they work together to create a seamless monitoring and visualization experience. What is Grafana? Grafana is an open-source platform for monitoring and observability, designed to visual
At Gumroad, we recently embarked on a new project called Helper. As the CEO, I was initially quite optimistic about using htmx for this project, even though some team members were less enthusiastic. My optimism stemmed from previous experiences with React, which often felt like overkill for our needs. I thought htmx could be a good solution to keep our front-end super light. Source with htmx - Cli
State of HTML 2023
While JavaScript was taking over the web, and CSS was gaining new superpowers year over year, it could seem like HTML was content to stay dormant, happy to cede center stage to its younger siblings. After all once you've learned about <div>s and <h>s 1 through 6, what else is there to know? Quite a lot, as it turns out! Once again we drafted Lea Verou to put her in-depth knowledge of the web platf
Datadog provides OSS community support for ruby-lang.org
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The Python Graph Gallery has always been a reservoir of inspiration, providing hundreds of foundational chart examples for newcomers and seasoned developers alike. While our vast collection offers a stepping stone into the world of data visualization, the following list stands out. Every chart here represents the pinnacle of craftsmanship, exhibiting the depths to which matplotlib can be customize
Bringing Flutter's Brilliance to JavaScript: Google's Genius, Your Power! "Flutter: Google's UI toolkit for building beautiful, natively compiled applications for mobile, web, and desktop from a single codebase." "Flitter brings Flutter's elegant and efficient API design to JavaScript, supercharging your web development." "Leverage 50+ ready-to-use widgets and a powerful rendering engine for stunn
K-means clustering is a type of unsupervised learning when we have unlabeled data (i.e., data without defined categories or groups). Clustering refers to a collection of data points based on specific similarities. K-Means Algorithm K-means aims to find groups in the data, with the number of groups represented by the variable K. Based on the provided features, the algorithm works iteratively to ass
Here is a list of common data engineering interview questions, with answers, which you may encounter for an interview as a data engineer. The questions during an interview for a data engineer aim to check not only the grasp of data systems and architectures but also a keen understanding of your technical prowess and problem-solving skills. This article lists essential interview questions and answe
Tableauはいいぞ - ブログ - 株式会社JADE
こんにちは。篠原です。 Googleのコアアップデートが3月5日にロールアウトされて、このタイミングでコアアップデートのレポートを書く予定だったのですが全然終わらないですね…。 そんな中の社内のスレッドがこちらです。 活発な社内スレッド。こうして今日も新しいアイデアが生まれる。 ということで「Tableauはいいぞ」になりました。 自分で言っておいてなんですが何書くの… Tableauはいいぞ そもそもTableauを知らないという方に紹介をすると、Tableauはデータ探索を行うためのBIツールです。 LookerStudioのようなものをイメージしていただくと分かりやすいかもしれません。 モダン BI 市場で選ばれている Tableau の分析プラットフォームは、データの探索と管理を簡単に行い、ビジネスや世界を変革する可能性があるインサイトを迅速に見出して共有することを可能にします。
K-nearest neighbors (k-NN) is a Machine Learning algorithm for supervised machine learning type. It is used for both regression and classification tasks. As we already know, a supervised machine learning algorithm depends on labeled input data, which the algorithm learns to produce accurate outputs when input unlabeled data. k-NN aims to predict the test data set by calculating the distance betwee
Building flexible data visualizations for international sites For our international clients, we have created dynamic charts and data visualizations for the web. Charts typically render shapes like lines and paths, rectangles and circles. They contain text for titles, axis labels, numerical values and legends. SVG is the good fit for this purpose. It embeds directly into HTML and pairs well with CS
A table of named individuals along with a select set of characteristics. This table arranges records containing personal characteristics as columns and rows. Each person is a row, and each characteristic makes up a different column. The characteristics use different types of data, like dates, numbers, and text. This arrangement makes it easy to look up individual values or make comparisons across
※この記事は、Luup Advent Calendar 2023 の25日目(最終日)の記事です。 こんにちは、株式会社Luup CTO の岡田(@7omich)です。 2年目もお陰様で完走へ向かっている Advent Calendar ですが、私は今年も最終日の記事を担当することになりました。 今年はさらに多彩なメンバーによって、様々な領域での発信が24記事も揃っています。 事業づくりに全力を注ぎながらもこれだけの発信量を外に出せるメンバーが引き続き集まってくれていることに感謝しつつ、発信されている内容を振り返っていくと、 (12/3公開) pmconf2023登壇 | 事業のターニングポイントに向けて、開発ロードマップの"策定"と"達成"のためにPMがやったこと (12/15公開) アプリの地図上へたくさんのピンを描画する技術 (12/22公開) インシデントマネジメントのこれまでとこ
Machine Learning is making a buzz in the industry. And it’s the right time to get familiar with it. Let’s get the basics right. Let’s get started. What is Machine Learning What the heck is machine learning? If I had to quote it in a single sentence, I would say, ‘Machine Learning is a way to find a pattern in data to predict the future. The above is not the only definition of machine learning. The
State of HTML 2023
While JavaScript was taking over the web, and CSS was gaining new superpowers year over year, it could seem like HTML was content to stay dormant, happy to cede center stage to its younger siblings. After all once you've learned about <div>s and <h>s 1 through 6, what else is there to know? Quite a lot, as it turns out! Once again we drafted Lea Verou to put her in-depth knowledge of the web platf
Arenas are industries that transform the business landscape. Eighteen future arenas could reshape the global economy and generate $29 trillion to $48 trillion in revenues by 2040. At a glance Arenas are a unique category of industries defined by two characteristics: high growth and dynamism. They capture an outsize share of the economy’s growth, and the market shares of players within them change
Introducing Spectrum 2: Our vision for the future of Adobe experience design
Introducing Spectrum 2: Our vision for the future of Adobe experience design A preview of the comprehensive update coming to Adobe's design system Imagine you’re designing a house. Your first step might be to draw a picture. To turn that picture into a house would require the input of many experts—architects, engineers, and builders—and would involve planning, teamwork, and time with each collabor
Rによる地理空間データの可視化
Rによる地理空間データの可視化 目的：R言語を用いて地理空間データを地図上に示すこと 対象：大学生，ゼミナール生 チュートリアル 1. 都道府県別データによる人口の可視化 1.1 追加データの可視化 1.2 自作データの可視化 1.3 対数による可視化 1.4 ジオメトリの移動による日本地図の可視化 ←（北海道・沖縄県の位置移動） 1.5 地方区分データの可視化 2. 地域メッシュ統計による人口密度の可視化 2.1 コミュニティバス路線上の人口密度の可視化 2.2 OpenStreetMapを背景にした情報通信業事業所数の可視化 2.3 土地利用細分メッシュデータによる土地利用状況の可視化 2.4 商圏内の地域メッシュデータの可視化 2.5 六角形マップによる人口密度の可視化 2.6 標高・傾斜度メッシュデータの可視化 3. 小地域統計による人口の可視化 3.1 小地域統計による人口変化の
The declarative paradigm is becoming ubiquitous in data engineering, to the point we sometimes feel we became YAML developers. Yet, I’ll argue it’s a good thing! Let’s take a step back and look at what it means to be declarative, and how it impacts the data systems we build. Data & logicFundamentally, a data platform is made of 2 pieces: Data. On the frontend, we find the actual files, tables, das
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postgres.new: In-browser Postgres with an AI interface
How video games use LUTs and how you can too
An easy look at Grafana architecture
</> htmx ~ Why Gumroad Didn't Choose htmx
Best Python Chart Examples
Flitter - Data Visualization Framework
K-Means Clustering for Unsupervised Machine Learning
Data Engineer: Interview Questions
k-NN (k-Nearest Neighbors) in Supervised Machine Learning
Responsive bar charts in HTML and CSS - 9elements
The Design Philosophy of Great Tables – great_tables
2023年の Luup 技術組織の現在地
Introduction to Machine Learning
The next big arenas of competition
YAML developers and the declarative data platforms