Context: Check out the README file first. This is an adapted excerpt from our research paper "The Case for Writing Network Drivers in High-Level Languages" [BibTeX]. Our Rust driver is a few percent slower than our C driver, why? Well, it's of course because of safety features in Rust, but can we quantify that? There are only two major differences between the idiomatic Rust and C implementations:
Dear visitor, this repository, issue tracker, etc. is abandoned in favor of https://github.com/Immediate-Mode-UI/Nuklear . Any activity in this issue tracker, any pull requests, etc. will be ignored. Looking forward to hearing from you in https://github.com/Immediate-Mode-UI/Nuklear Nuklear community This is a minimal state immediate mode graphical user interface toolkit written in ANSI C and lice
oneDNN Developer Guide and Reference explains the programming model, supported functionality, implementation details, and includes annotated examples. API Reference provides a comprehensive reference of the library API. Release Notes explain the new features, performance optimizations, and improvements implemented in each version of oneDNN. oneDNN supports platforms based on the following architec
Interested in adding textures, lighting, shadows, normal maps, glowing objects, ambient occlusion, reflections, refractions, and more to your 3D game? Great! Below is a collection of shading techniques that will take your game visuals to new heights. I've explained each technique in such a way that you can take what you learn here and apply/port it to whatever stack you use—be it Godot, Unity, Unr
A curated list of tools, articles, books, and any other resource related to code review Code review is the systematic examination (sometimes referred to as peer review) of computer source code. An experiment to assess the cost-benefits of code inspections in large scale software development (Porter, Siy, Toman & Votta, 1997) Early paper that tested a range of then-current review techniques includi
MNIST 言わずと知れた手書き文字のデータ CIFAR-10 言わずと知れた10クラス(airplane, automobileなど)にラベル付された画像集。CIFAR-100というより詳細なラベル付けがされたものもある The Oxford-IIIT Pet Dataset CIFAR-10と同様、ラベル付きのデータ。その名の通り動物系 Fashion-MNIST ファッション画像のMNIST、を表したデータセット。クラス数はMNISTと同様10クラスで、画像は28x28(グレースケール)、学習:評価データ数は60,000:10,000。 MNISTは簡単すぎる、濫用されているといった問題を克服するという側面も意識されている。 iMaterialist Challenge on fashion 100万点をこえるファッション画像のデータセット。8グループ228のラベルがアノテーションさ
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