Awesome GitHub RepositoriesModel Finetuning

AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc

Gluon-CV 是一个 MXNet 计算机视觉库，提供了一系列预实现的视觉架构和训练管线。它作为一个深度学习研究工具包和模型库，包含用于图像和视频分析的最先进预训练权重。 该项目包括一个专门的人体姿态估计库和模型压缩工具包。这些工具允许对深度学习模型进行剪枝和量化，以提高推理速度并促进在受限边缘硬件上的部署。 该库涵盖了广泛的视觉功能，包括图像分类、目标检测以及语义和实例分割。它还提供视频分析工具，如动作识别、目标跟踪和单目深度估计。 训练通过自动化管线和分布式多 GPU 工作负载提供支持，以加速模型收敛。

ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself

Marqo 是一个电子商务产品发现平台、多模态向量数据库和 AI 搜索营销工具。它为实现语义搜索和推荐提供了基础设施，允许购物者使用自然语言和图像查找产品。 该平台通过结合神经语义分数与业务定义的提升和置顶规则的混合排名流水线脱颖而出。它具有一个对话式商务引擎，使用大语言模型来处理用户意图，并提供搜索性能分析套件，用于通过 A/B 测试衡量转化率提升和收入。 其更广泛的功能包括用于统一文本和图像检索的多模态索引、基于点击流数据优化排名的自动化行为学习以及个性化推荐引擎。该系统还涵盖了目录同步、基于属性的切面聚合以及对话式购物摘要的生成。

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

本项目是一个富有表现力的文本转语音基础模型和语音克隆系统，旨在合成具有情感细微差别和高保真度的人类语音。它作为一个可微调的语音模型，可以使用参考语音样本生成模仿特定人的音频。 该系统通过高性能推理引擎脱颖而出，该引擎利用内存缓存和硬件编译来减少音频生成过程中的延迟。它进一步允许通过在由音频文件和匹配字幕组成的自定义数据集上训练语言模型来提高合成质量。 该框架涵盖了自定义语音克隆、表现力语音合成和语音模型微调的更广泛领域。

This project is a deep learning toolkit designed for audio source separation and music information retrieval. It provides a framework for decomposing polyphonic audio signals into distinct components, such as vocals, drums, and bass, by processing raw waveforms through neural network architectures. The library enables users to train custom separation models or fine-tune existing ones to improve accuracy on specific audio datasets. It supports the entire model lifecycle, including the conversion of raw audio into structured, indexed formats to optimize data loading and training efficiency. Th