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  • Gamedev in Lisp. Part 1: ECS and Metalinguistic Abstraction - cl-fast-ecs by Andrew

    Gamedev in Lisp. Part 1: ECS and Metalinguistic Abstraction In this series of tutorials, we will delve into creating simple 2D games in Common Lisp. The result of the first part will be a development environment setup and a basic simulation displaying a 2D scene with a large number of physical objects. It is assumed that the reader is familiar with some high-level programming language, has a gener

      Gamedev in Lisp. Part 1: ECS and Metalinguistic Abstraction - cl-fast-ecs by Andrew
    • 0.8.0 Release Notes ⚡ The Zig Programming Language

      Tier 4 Support § Support for these targets is entirely experimental. If this target is provided by LLVM, LLVM may have the target as an experimental target, which means that you need to use Zig-provided binaries for the target to be available, or build LLVM from source with special configure flags. zig targets will display the target if it is available. This target may be considered deprecated by

      • VSeeFace

        Contents About Download Terms of use Credits VSFAvatar Tutorials Manual FAQ Virtual camera Transparency Network tracking Special blendshapes Expressions VMC protocol Model posing iPhone tracking Perception Neuron ThreeDPoseTracker Troubleshooting Preview in Unity Translations Running on Linux Troubleshooting Startup Tracking/Webcam Virtual camera Model issues Lipsync Game capture Log folder Perfor

        • はじめての自然言語処理 spaCy 3.0 で Transformer を利用する | オブジェクトの広場

          今更ですが今年の2月に spaCy 3.0 が公開されました。 3.0 で導入された新機能の中で目玉と言えるのは、やはり Hugging Face Transformers (以下、単にTransformers) のサポートや PyTorch, Tensorflow との連携になるでしょう。今回はその辺りを実際に学習を動かしながら紹介したいと思います。 1. はじめに 今回は今年の2月に公開された spaCy 3.0 の話です。 spaCy は第4回でも紹介しましたが、研究者向けというよりは自然言語処理アプリ開発者向けのオープンソース自然言語処理ライブラリになります。日本語を含めた様々な言語の学習済みモデルが存在しており、 spaCy をインストールして、学習済みモデルをダウンロードするだけで、分かち書き、品詞や依存関係の推定、単語や文の類似度の判定など様々な機能を使用することができます。

            はじめての自然言語処理 spaCy 3.0 で Transformer を利用する | オブジェクトの広場
          • 0.10.0 Release Notes ⚡ The Zig Programming Language

            Tier 4 Support § Support for these targets is entirely experimental. If this target is provided by LLVM, LLVM may have the target as an experimental target, which means that you need to use Zig-provided binaries for the target to be available, or build LLVM from source with special configure flags. zig targets will display the target if it is available. This target may be considered deprecated by

            • The Best GPUs for Deep Learning in 2023 — An In-depth Analysis

              Deep learning is a field with intense computational requirements, and your choice of GPU will fundamentally determine your deep learning experience. But what features are important if you want to buy a new GPU? GPU RAM, cores, tensor cores, caches? How to make a cost-efficient choice? This blog post will delve into these questions, tackle common misconceptions, give you an intuitive understanding

                The Best GPUs for Deep Learning in 2023 — An In-depth Analysis
              • What's New in Emacs 28.1?

                Try Mastering Emacs for free! Are you struggling with the basics? Have you mastered movement and editing yet? When you have read Mastering Emacs you will understand Emacs. It’s that time again: there’s a new major version of Emacs and, with it, a treasure trove of new features and changes. Notable features include the formal inclusion of native compilation, a technique that will greatly speed up y

                • 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
                  • GitHub - ComfyUI-Workflow/awesome-comfyui: A collection of awesome custom nodes for ComfyUI

                    ComfyUI-Gemini_Flash_2.0_Exp (⭐+172): A ComfyUI custom node that integrates Google's Gemini Flash 2.0 Experimental model, enabling multimodal analysis of text, images, video frames, and audio directly within ComfyUI workflows. ComfyUI-ACE_Plus (⭐+115): Custom nodes for various visual generation and editing tasks using ACE_Plus FFT Model. ComfyUI-Manager (⭐+113): ComfyUI-Manager itself is also a cu

                      GitHub - ComfyUI-Workflow/awesome-comfyui: A collection of awesome custom nodes for ComfyUI
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