Maschinelles Lernen · GitHub

Kürbisbuch: Ausführliche Erläuterungen zu den Formeln in Machine Learning (Wassermelonenbuch)

Deepfakes-Software für alle

Self-host the powerful Chatterbox TTS model. This server offers a user-friendly Web UI, flexible API endpoints (incl. OpenAI compatible), predefined voices, voice cloning, and large audiobook-scale text processing. Runs accelerated on NVIDIA (CUDA), AMD (ROCm), and CPU.

• DGX Spark / sm_121 support via the new docker-compose-cu130.yml (CUDA 13.0, PyTorch 2.10). RTX 30/40/50 keep using cu121 / cu128.
• AMD Strix Halo support via docker-compose-strixhalo.yml (ROCm 7.2, HSA_OVERRIDE_GFX_VERSION=11.0.0).

Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions.

QualityScaler - KI-App zur Hochskalierung von Bildern und Videos

• Elegant and easy to use GUI
• Image and video upscale

深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.

🔥 LeetCode for PyTorch — practice implementing softmax, attention, GPT-2 and more from scratch with instant auto-grading. Jupyter-based, self-hosted or try online.

Vollständige Referenz zu den Antworten für LinkedIn-Skill-Assessments 2024 (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) – einschließlich Lösungen für den LinkedIn-Excel-Test, den LinkedIn-Machine-Learning-Test sowie Fragen und Antworten zu LinkedIn-Tests

A tutorial showing how to train, convert, and run TensorFlow Lite object detection models on Android devices, the Raspberry Pi, and more!

How to train a TensorFlow Object Detection Classifier for multiple object detection on Windows

🔊 Kokoro Web: Free AI text-to-speech, online or self-hosted, OpenAI compatible!

• Zero Installation - Works directly in your browser, no downloads needed
• Self Hostable - If you need an OpenAI Compatible API

DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained in a diffusion world model. NeurIPS 2024 Spotlight.

computer vision projects | 计算机视觉相关好玩的AI项目（Python、C++、embedded system）

机器人视觉 移动机器人 VS-SLAM ORB-SLAM2 深度学习目标检测 yolov3 行为检测 opencv PCL 机器学习 无人驾驶

BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.

• A test CI with integration and training test routines that are run for all simulators and algorithms
• Integration in the official TorchRL ecosystem for dedicated support

A modular high-level library to train embodied AI agents across a variety of tasks and environments.

PyTorch code and models for V-JEPA self-supervised learning from video.

A Full Stack ML (Machine Learning) Roadmap involves learning the necessary skills and technologies to become proficient in all aspects of machine learning, including data collection and preprocessing, model development, deployment, and maintenance.