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AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
An open-source remote desktop application designed for self-hosting, as an alternative to TeamViewer.
中英文敏感词、语言检测、中外手机/电话归属地/运营商查询、名字推断性别、手机号抽取、身份证抽取、邮箱抽取、中日文人名库、中文缩写库、拆字词典、词汇情感值、停用词、反动词表、暴恐词表、繁简体转换、英文模拟中文发音、汪峰歌词生成器、职业名称词库、同义词库、反义词库、否定词库、汽车品牌词库、汽车零件词库、连续英文切割、各种中文词向量、公司名字大全、古诗词库、IT词库、财经词库、成语词库、地名词库、…
为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, m…
A natural language interface for computers
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Foundational Models for State-of-the-Art Speech and Text Translation
TikTok 無需拔卡解鎖最新支援 iPhone &iPad 、TikTok&TikTok TestFlight，地區切換 、視頻發佈 、 live 直播 、點贊 評論、私信聊天等！
This is an AI agent for Street Fighter II Champion Edition.
A curated list of awesome self-supervised methods
A collection of AWESOME things about domain adaptation
标注自己的数据集，训练、评估、测试、部署自己的人工智能算法
Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization
互联网仍有记忆！那些曾经在校招过程中毁过口头offer、意向书、三方的公司！纵然人微言轻，也想尽绵薄之力！
😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.
Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)
A graphical user interface for AutoGPT
A collection of implementations of deep domain adaptation algorithms
⚡️利用 Shortcut 在 iOS / iPadOS / macOS 端使用 Apple News.
[NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
A collection of implementations of adversarial domain adaptation algorithms
Domain-Adversarial Neural Network in Tensorflow
Awesome things about domain generalization, including papers, code, etc.
A PyTorch implementation for Adversarial Discriminative Domain Adaptation
code released for our ICML 2020 paper "Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation"