Maschinelles Lernen · GitHub

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✨ Build a machine learning model from a prompt

[CVPR'24 Oral] Official repository of Point Transformer V3 (PTv3). Also a pip-installable PyTorch + Triton package with pretrained Sonata, Concerto, and Utonia encoders.

• Oct 2026: PTv3 is now a pip package, point-transformer-v3, with optimizations from Pointcept2, our next codebase. It needs no spconv or FlashAttention (plain PyTorch with Triton kernels), runs 1.7-4.7x faster with 18-31% less memory on the GPU, also runs on the CPU, and loads the Sonata, Concerto and Utonia weights. See docs/acceleration.md.
• Apr 2024: PTv3 is selected as one of the 90 Oral presentations of CVPR'24.

This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc

Collective communications library with various primitives for multi-machine training.

High-Level-Bibliothek zum flexiblen und transparenten Trainieren und Evaluieren neuronaler Netze in PyTorch.

• Less code than pure PyTorch while ensuring maximum control and simplicity
• Library approach and no program's control inversion - Use ignite where and when you need

🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)

AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.

Crater is a cloud-native AI training & inference platform.

NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥

Eine Sammlung von Tutorials und Beispielen zum Lösen und Verstehen von Aufgaben des maschinellen Lernens und der Musterklassifikation

The "Python Machine Learning (1st edition)" book code repository and info resource

Best Practices für Empfehlungssysteme

Android Application Identifier for Packers, Protectors, Obfuscators and Oddities - PEiD for Android

The Fastest Deep Reinforcement Learning Library

探索类开源：SuperMimic与MimicAgent联动用于四轮足机械臂的统一协同控制与分层控制的训练方案。欢迎交流与共同探索。

The official repository for ROOT: analyzing, storing and visualizing big data, scientifically

A unified, comprehensive and efficient recommendation library