PyTorch Profiler With TensorBoard — PyTorch Tutorials 2.3.0+cu121 documentation
PyTorch Recipes See All Recipes See All Prototype Recipes Introduction to PyTorch Learn the Basics Quickstart Tensors Datasets & DataLoaders Transforms Build the Neural Network Automatic Differentiation with torch.autograd Optimizing Model Parameters Save and Load the Model Introduction to PyTorch on YouTube Introduction to PyTorch - YouTube Series Introduction to PyTorch Introduction to PyTorch T
Send feedback Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. �u规U "秄U
Set up authorization rules - JavaScript - AWS Amplify Documentation
You are currently viewing the legacy GraphQL Transformer documentation. View latest documentation @auth Authorization is required for applications to interact with your GraphQL API. API Keys are best used for public APIs (or parts of your schema which you wish to be public) or prototyping, and you must specify the expiration time before deploying. IAM authorization uses Signature Version 4 to make
Updating an App to Use Swift Concurrency | Apple Developer Documentation
Improve your app’s performance by refactoring your code to take advantage of asynchronous functions in Swift.
Denoising Diffusion Implicit Models Author: András Béres Date created: 2022/06/24 Last modified: 2022/06/24 Description: Generating images of flowers with denoising diffusion implicit models. View in Colab • GitHub source Introduction What are diffusion models? Recently, denoising diffusion models, including score-based generative models, gained popularity as a powerful class of generative models,
GitHub - kkoomen/vim-doge: (Do)cumentation (Ge)nerator for nearly 20 languages 📚 Generate proper code documentation with a single keypress. ⚡️🔥
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We rebuilt Cloudflare's developer documentation - here's what we learned
We rebuilt Cloudflare's developer documentation - here's what we learned05/27/2022 We recently updated developers.cloudflare.com, the Cloudflare Developers documentation website, to a new version of our custom documentation engine. This change consisted of a significant migration from Gatsby to Hugo and converged a collection of Workers Sites into a single Cloudflare Pages instance. Together, thes
Google Images SEO: Image Metadata | Google Search Central | Documentation | Google for Developers
Send feedback Stay organized with collections Save and categorize content based on your preferences. Image metadata in Google Images When you specify image metadata, Google Images can show more details about the image, such as who the creator is, how people can use an image, and credit information. For example, providing licensing information can make the image eligible for the Licensable badge, w
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WebKit Documentation
WebKit Overview WebKit Overview Build & Debug Deep Dive Getting Started Infrastructure Other Ports WebKit Overview¶ WebKit is a cross-platform web browser engine. On iOS and macOS, it powers Safari, Mail, iBooks, and many other applications. Getting Up and Running¶ Downloading the Source Code¶ git clone https://github.com/WebKit/WebKit.git WebKit Building WebKit¶ Compilation instructions are for m
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
(optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime — PyTorch Tutorials 2.3.0+cu121 documentation
Learn Get Started Run PyTorch locally or get started quickly with one of the supported cloud platforms Tutorials Whats new in PyTorch tutorials Learn the Basics Familiarize yourself with PyTorch concepts and modules PyTorch Recipes Bite-size, ready-to-deploy PyTorch code examples Intro to PyTorch - YouTube Series Master PyTorch basics with our engaging YouTube tutorial series
pandas.DataFrame.dropna# DataFrame.dropna(*, axis=0, how=_NoDefault.no_default, thresh=_NoDefault.no_default, subset=None, inplace=False, ignore_index=False)[source]# Remove missing values. See the User Guide for more on which values are considered missing, and how to work with missing data. Parameters: axis{0 or ‘index’, 1 or ‘columns’}, default 0Determine if rows or columns which contain missing
Use vectors to calculate geometric values, calculate dot products and cross products, and interpolate between values.
DevTools を Internet Explorer モード (IE モード) で使用する - Microsoft Edge Developer documentation
Internet Explorer モード (IE モード) は、Microsoft Edge DevTools と統合されます。 IE モードでは、企業は Internet Explorer 11 でのみ動作する Web サイトの一覧を指定できます。 Microsoft Edge でこれらの Web サイトに移動すると、Internet Explorer 11 のインスタンスが実行され、タブにサイトがレンダリングされます。 IE モードを使用すると、企業は最新の Web ブラウザーと現在互換性のないテクノロジとの互換性を管理できます。 IE モードには、次のテクノロジのサポートが含まれています。 IE ドキュメント モード。 ActiveX コントロール。 その他のレガシ コンポーネント。 IE モードでは、レンダリング プロセスは Internet Explorer 11 に基づいてい
Google's Engineering Practices documentationの真似したいプラクティス 1 - Qiita
Google's Engineering Practices documentationはGoogleのコードレビュープロセスとポリシーの標準的な説明のドキュメントです。 https://google.github.io/eng-practices/review/ 開発の仕事する中でコードレビューにかける時間は長いですよね？でも雰囲気でレビューしていませんか？ 何をどのようにコードレビューするのかは、ちゃんと知っておきたい部分でもあり、チームで共有したいものでもあります。 いろいろ書いてあるのですが、個人的に刺さる部分を書いていきます。なので、結構端折ってます。意味違うのではみたいなのがあれば修正するので教えて下さい。 以下のような構成になっており、今回はコードレビューの方法の コードレビューの見方(Navigating a CL in Review)まで見ていきます。 コードレビューの方
Google won’t comment on a potentially massive leak of its search algorithm documentation
Google’s search algorithm is perhaps the most consequential system on the internet, dictating what sites live and die and what content on the web looks like. But how exactly Google ranks websites has long been a mystery, pieced together by journalists, researchers, and people working in search engine optimization. Now, an explosive leak that purports to show thousands of pages of internal document
Prisma Client supports both offset pagination and cursor-based pagination. Offset pagination Offset pagination uses skip and take to skip a certain number of results and select a limited range. The following query skips the first 3 Post records and returns records 4 - 7: const results = await prisma.post.findMany({ skip: 3, take: 4, }) To implement pages of results, you would just skip the number
JAX: High-Performance Array Computing# JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. If you’re looking to train neural networks, use Flax and start with its documentation. Some associated tools are Optax and Orbax. For an end-to-end transformer library built on JAX,
In this Quickstart guide, you'll learn how to get started with Prisma ORM from scratch using a plain TypeScript project and a local SQLite database file. It covers data modeling, migrations and querying a database. If you want to use Prisma ORM with your own PostgreSQL, MySQL, MongoDB or any other supported database, go here instead: Start with Prisma ORM from scratch Add Prisma ORM to an existing
Community tutorials# This is a guide to many pandas tutorials by the community, geared mainly for new users. pandas cookbook by Julia Evans# The goal of this 2015 cookbook (by Julia Evans) is to give you some concrete examples for getting started with pandas. These are examples with real-world data, and all the bugs and weirdness that entails. For the table of contents, see the pandas-cookbook Git
Porting Your macOS Apps to Apple Silicon | Apple Developer Documentation
Create a version of your macOS app that runs on both Apple silicon and Intel-based Mac computers.
Displaying a Point Cloud Using Scene Depth | Apple Developer Documentation
Present a visualization of the physical environment by placing points based a scene’s depth data.
最近 hitode909 さんが、ここ最近ドキュメントについて書いているのを読んで、 Creating Documentation in an Agile Scrum Environment チームのScrapbox3000ページくらいを見返して整理した The Product is Docs: Writing technical documentation in a product development group そういえば、Increment が Documentation 特集だった号 があったなあと思い出したので、読んでみた。 Increment は Stripe のやっている雑誌 (という話は2017年にも書いた)。もともとはオンライン雑誌だったのだけど、今は紙版も売っている。といっても Airbnb Magazine や Offscreen のように、実際に本屋で売っていた
The following deliverables are not in any particular order: Add bindings for more devices Add/Test support on more boards Onboard more customers and work on making them successful Improve the Eventing APIs for the SysFS driver for GPIO (#378) Add infrastructure required for servicing bindings package Improve documentation for onboarding, adding device bindings, working with samples Improve the abs
Working with preprocessing layers Authors: Francois Chollet, Mark Omernick Date created: 2020/07/25 Last modified: 2021/04/23 Description: Overview of how to leverage preprocessing layers to create end-to-end models. View in Colab • GitHub source Keras preprocessing The Keras preprocessing layers API allows developers to build Keras-native input processing pipelines. These input processing pipelin
JupyterLab Changelog — JupyterLab 4.2.0b0 documentation
JupyterLab Changelog# v4.1# JupyterLab 4.1 includes a number of new features (described below), bug fixes, and enhancements for extension developers. This release is compatible with extensions supporting JupyterLab 4.0. Extension authors are recommended to consult the Extension Migration Guide which lists deprecations and changes to the public API. Custom CSS# JupyterLab now supports automatic loa
Welcome to Invoke!¶ Invoke is a Python (2.7 and 3.4+) library for managing shell-oriented subprocesses and organizing executable Python code into CLI-invokable tasks. It draws inspiration from various sources (make/rake, Fabric 1.x, etc) to arrive at a powerful & clean feature set. To find out what’s new in this version of Invoke, please see the changelog. The project maintainer keeps a roadmap on
All Skin Parameters — Ashley's PlantUML Doc 0.2.01 documentation
All Skin Parameters¶ All “skinparams” (skin parameters) available in PlantUML in alphabetical order.
© Copyright 2001-2024, Python Software Foundation. This page is licensed under the Python Software Foundation License Version 2. Examples, recipes, and other code in the documentation are additionally licensed under the Zero Clause BSD License. See History and License for more information. The Python Software Foundation is a non-profit corporation. Please donate. Last updated on May 06, 2024 (01:5
Introducing WebKit Documentation
Today, we are excited to announce docs.webkit.org, a new documentation home for the WebKit developer community. WebKit is a massive open source project containing many subsystems, and coming up to speed as a new contributor or diving into a new subsystem can be quite a daunting endeavor. This documentation’s focus is on exposing the internals of the web engine in an easy to understand matter. To d
GitHub - reconquest/shdoc: Documentation generator for shell scripts (bash, sh, zsh). Javadoc for shell scripts.
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GitHub - microsoft/coding-pack-for-python: Documentation and issues for the VS Code Python installer.
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Using the latest code signature format | Apple Developer Documentation
Update legacy app code signatures so your app runs on current OS releases.
Quick Start Guide - Strapi Developer Docs | Strapi Documentation
Strapi offers a lot of flexibility. Whether you want to go fast and quickly see the final result, or would rather dive deeper into the product, we got you covered. For this tutorial, we'll go for the DIY approach and build a project and data structure from scratch, then deploy your project to Strapi Cloud to add data from there. Before installing Strapi, the following requirements must be installe
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Home | D2 Documentation
D2: Declarative DiagrammingA modern language that turns text to diagrams Explore docs Create beautiful diagrams in minutes. Simple syntax. Endlessly customizable. D2 is the fastest and easiest way to get a mental model from your head onto the screen, then make edits with your team. NETWORKUSERAPI SERVERLOGSCELL TOWERONLINE PORTALDATA PROCESSORSATELLITESTRANSMITTERUISTORAGE SENDSENDSENDPHONE LOGSMA
Documentation — AWS Chalice
[full example] from chalice import Chalice app = Chalice(app_name="helloworld") @app.route("/") def index(): return {"hello": "world"} @app.schedule(Rate(5, unit=Rate.MINUTES)) def periodic_task(event): return {"hello": "world"} @app.on_s3_event(bucket='mybucket') def s3_handler(event): print(event.bucket, event.key) Focus on writing your application code Focus on writing your application code ins
matplotlib.pyplot.imshow# matplotlib.pyplot.imshow(X, cmap=None, norm=None, *, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, interpolation_stage=None, filternorm=True, filterrad=4.0, resample=None, url=None, data=None, **kwargs)[source]# Display data as an image, i.e., on a 2D regular raster. The input may either be actual RGB(A) data, or 2D scalar da
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Quickstarts | Cloud Run Documentation | Google Cloud
Keras documentation: Denoising Diffusion Implicit Models
Quickstart - Boto3 1.34.105 documentation
Welcome to Faiss Documentation — Faiss documentation
AI Command Suggestions | Warp Documentation
pandas.DataFrame.dropna — pandas 2.2.2 documentation
Working with Vectors | Apple Developer Documentation
Pagination (Reference) | Prisma Documentation
JAX: High-Performance Array Computing — JAX documentation
Quickstart with TypeScript & SQLite | Prisma Documentation
Community tutorials — pandas 1.5.1 documentation
Increment の Documentation 号を読んだ - blog.8-p.info
iot/Documentation/roadmap.md at main · dotnet/iot
Keras documentation: Working with preprocessing layers
Welcome to Invoke! — Invoke documentation
Search — Python 3.12.3 documentation
matplotlib.pyplot.imshow — Matplotlib 3.9.0 documentation