{"id":15550065,"url":"https://github.com/hscspring/all4ai","last_synced_at":"2025-09-08T02:31:22.169Z","repository":{"id":39679929,"uuid":"116469351","full_name":"hscspring/ALL4AI","owner":"hscspring","description":"AI Related Tools/Projects","archived":false,"fork":false,"pushed_at":"2025-04-20T15:50:59.000Z","size":292,"stargazers_count":25,"open_issues_count":0,"forks_count":3,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-20T16:35:58.946Z","etag":null,"topics":["ai","jupyter","linux","machine-learning","nlp","python","ssh","toolbox"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-2-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/hscspring.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2018-01-06T09:09:02.000Z","updated_at":"2025-04-20T15:51:03.000Z","dependencies_parsed_at":"2024-10-28T14:56:53.554Z","dependency_job_id":"bfa1f1ba-a9e2-4702-8d95-4d1fda4d00d7","html_url":"https://github.com/hscspring/ALL4AI","commit_stats":{"total_commits":105,"total_committers":3,"mean_commits":35.0,"dds":0.05714285714285716,"last_synced_commit":"7704c67cb3822c96d0e020b75544a707fadd962d"},"previous_names":["hscspring/all4ai"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hscspring/ALL4AI","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hscspring%2FALL4AI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hscspring%2FALL4AI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hscspring%2FALL4AI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hscspring%2FALL4AI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hscspring","download_url":"https://codeload.github.com/hscspring/ALL4AI/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hscspring%2FALL4AI/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274123019,"owners_count":25226033,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-08T02:00:09.813Z","response_time":121,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","jupyter","linux","machine-learning","nlp","python","ssh","toolbox"],"created_at":"2024-10-02T13:49:32.576Z","updated_at":"2025-09-08T02:31:22.153Z","avatar_url":"https://github.com/hscspring.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003c!-- START doctoc generated TOC please keep comment here to allow auto update --\u003e\n\u003c!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE --\u003e\n**Table of Contents**  *generated with [DocToc](https://github.com/thlorenz/doctoc)*\n\n- [Video](#video)\n- [LLM](#llm)\n  - [CTG](#ctg)\n  - [Layout](#layout)\n  - [Sheet](#sheet)\n  - [Agent](#agent)\n  - [VectorSearch](#vectorsearch)\n  - [Just Skin](#just-skin)\n  - [Inference](#inference)\n  - [Prompts](#prompts)\n- [AI](#ai)\n  - [Format](#format)\n  - [Inference](#inference-1)\n  - [Deploy](#deploy)\n  - [Demo/WebAPP](#demowebapp)\n  - [Toolkit](#toolkit)\n  - [Dataset](#dataset)\n  - [DataAnnotation](#dataannotation)\n  - [DeepLearning](#deeplearning)\n  - [MachineLearning](#machinelearning)\n  - [HyperOptimization](#hyperoptimization)\n  - [TTS](#tts)\n\n\u003c!-- END doctoc generated TOC please keep comment here to allow auto update --\u003e\n\n\u003e 关注AI领域值得关注的研究进展，包括视频、LLM及相关应用，以及其他AI相关。\n\n## Agent\n\n### Application\n\n- [awesome-deepseek-integration/README_cn.md at main · deepseek-ai/awesome-deepseek-integration](https://github.com/deepseek-ai/awesome-deepseek-integration/blob/main/README_cn.md)\n- [camel-ai/camel: 🐫 CAMEL: Finding the Scaling Law of Agents. The first and the best multi-agent framework. https://www.camel-ai.org](https://github.com/camel-ai/camel)\n- [mannaandpoem/OpenManus: No fortress, purely open ground. OpenManus is Coming.](https://github.com/mannaandpoem/OpenManus)\n- [browser-use/browser-use: Make websites accessible for AI agents](https://github.com/browser-use/browser-use)\n- [OpenInterpreter/open-interpreter: A natural language interface for computers](https://github.com/OpenInterpreter/open-interpreter)\n- [OpenInterpreter/01: The #1 open-source voice interface for desktop, mobile, and ESP32 chips.](https://github.com/OpenInterpreter/01)\n- [yuruotong1/autoMate: like manus and omniparser.AI-driven local automation assistant that uses natural language to make computers work by themselves](https://github.com/yuruotong1/autoMate/tree/master)\n\n### Framework\n\n重点关注MicroSoft。\n\n- [geekan/MetaGPT: 🌟 The Multi-Agent Framework: First AI Software Company, Towards Natural Language Programming](https://github.com/geekan/MetaGPT)\n- [microsoft/autogen: Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ](https://github.com/microsoft/autogen)\n- [langchain-ai/langgraph: Build resilient language agents as graphs.](https://github.com/langchain-ai/langgraph)\n- [crewAIInc/crewAI: Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.](https://github.com/crewAIInc/crewAI)\n- [Significant-Gravitas/AutoGPT: 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.](https://github.com/Significant-Gravitas/AutoGPT)\n- [LLaVA-VL/LLaVA-Interactive-Demo: LLaVA-Interactive-Demo](https://github.com/LLaVA-VL/LLaVA-Interactive-Demo/tree/main)\n- [yxuansu/PandaGPT: [TLLM'23] PandaGPT: One Model To Instruction-Follow Them All](https://github.com/yxuansu/PandaGPT/tree/main)\n- [microsoft/semantic-kernel: Integrate cutting-edge LLM technology quickly and easily into your apps](https://github.com/microsoft/semantic-kernel/tree/main)\n- [visual-openllm/visual-openllm: something like visual-chatgpt, 文心一言的开源版](https://github.com/visual-openllm/visual-openllm)\n\n### KnowldgeBase\n\n- [1Panel-dev/MaxKB: 💬 Ready-to-use \u0026 flexible RAG Chatbot, supporting mainstream large language models (LLMs) such as DeepSeek-R1, Llama 3.3, Qwen2, OpenAI and more.](https://github.com/1Panel-dev/MaxKB)\n- [outline/outline: The fastest knowledge base for growing teams. Beautiful, realtime collaborative, feature packed, and markdown compatible.](https://github.com/outline/outline)\n- [infiniflow/ragflow: RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.](https://github.com/infiniflow/ragflow)\n\n## Video\n\n- `OpenSource` [THUDM/CogVideo: text and image to video generation: CogVideoX (2024) and CogVideo (ICLR 2023)](https://github.com/THUDM/CogVideo)\n- `OpenSource` [genmo/mochi-1-preview · Hugging Face](https://huggingface.co/genmo/mochi-1-preview)\n- `OpenSource` [rain1011/pyramid-flow-miniflux · Hugging Face](https://huggingface.co/rain1011/pyramid-flow-miniflux)\n- `OpenSource` [Lightricks/LTX-Video · Hugging Face](https://huggingface.co/Lightricks/LTX-Video)\n- `OpenSource` [rhymes-ai/Allegro: Allegro is a powerful text-to-video model that generates high-quality videos up to 6 seconds at 15 FPS and 720p resolution from simple text input.](https://github.com/rhymes-ai/Allegro)\n- `OpenSource` [PKU-YuanGroup/Open-Sora-Plan: This project aim to reproduce Sora (Open AI T2V model), we wish the open source community contribute to this project.](https://github.com/PKU-YuanGroup/Open-Sora-Plan)\n- `OpenModel, TrainOnRoad` [TMElyralab/MuseV: MuseV: Infinite-length and High Fidelity Virtual Human Video Generation with Visual Conditioned Parallel Denoising](https://github.com/TMElyralab/MuseV)\n- `OpenModel` [stepfun-ai/Step-Video-T2V](https://github.com/stepfun-ai/Step-Video-T2V/tree/main)\n- `OpenModel` [tencent/HunyuanVideo-I2V · Hugging Face](https://huggingface.co/tencent/HunyuanVideo-I2V)\n- `OpenModel` [Wan-Video/Wan2.1: Wan: Open and Advanced Large-Scale Video Generative Models](https://github.com/Wan-Video/Wan2.1)\n\n## Image\n\n- `OpenModel` [CohereForAI/aya-vision-8b · Hugging Face](https://huggingface.co/CohereForAI/aya-vision-8b)\n- `OpenModel, TrainOnRoad` [THUDM/CogView4-6B · Hugging Face](https://huggingface.co/THUDM/CogView4-6B)\n\n### OCR\n\n- [Alpha-Innovator/OmniCaptioner](https://github.com/Alpha-Innovator/OmniCaptioner)\n- [microsoft/OmniParser: A simple screen parsing tool towards pure vision based GUI agent](https://github.com/microsoft/OmniParser)\n- [olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models](https://arxiv.org/abs/2502.18443)\n\n## LLM\n\n### CTG\n\n- [Saibo-creator/Awesome-LLM-Constrained-Decoding: A curated list of papers related to constrained decoding of LLM, along with their relevant code and resources.](https://github.com/Saibo-creator/Awesome-LLM-Constrained-Decoding)\n\n### Layout\n\n- [unilm/layoutreader at master · microsoft/unilm](https://github.com/microsoft/unilm/tree/master/layoutreader)\n- [PaddleNLP/model_zoo/ernie-layout/README_ch.md at develop · PaddlePaddle/PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/model_zoo/ernie-layout/README_ch.md)\n- [AdvancedLiterateMachinery/Applications/DocXChain at main · AlibabaResearch/AdvancedLiterateMachinery](https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/Applications/DocXChain/)\n\n### Sheet\n\n- [gventuri/pandas-ai: PandasAI is the Python library that integrates Gen AI into pandas, making data analysis conversational](https://github.com/gventuri/pandas-ai)\n\n### VectorSearch\n\n- [paradedb/paradedb: PostgreSQL for Search](https://github.com/paradedb/paradedb)\n- [facebookresearch/faiss: A library for efficient similarity search and clustering of dense vectors.](https://github.com/facebookresearch/faiss)\n- [milvus-io/milvus: A cloud-native vector database, storage for next generation AI applications](https://github.com/milvus-io/milvus)\n- [alibaba/proxima](https://github.com/alibaba/proxima)\n- [vearch/vearch: A distributed system for embedding-based vector retrieval](https://github.com/vearch/vearch)\n- [castorini/anserini: Anserini is a Lucene toolkit for reproducible information retrieval research](https://github.com/castorini/Anserini)\n- [google-research/scann at master · google-research/google-research](https://github.com/google-research/google-research/tree/master/scann)\n\n### Just Skin\n\n- [terry3041/pyChatGPT: An unofficial Python wrapper for OpenAI's ChatGPT API](https://github.com/terry3041/pyChatGPT)\n- [acheong08/EdgeGPT: Reverse engineered API of Microsoft's Bing Chat AI](https://github.com/acheong08/EdgeGPT)\n- [acheong08/ChatGPT: Reverse engineered ChatGPT API](https://github.com/acheong08/ChatGPT)\n- [transitive-bullshit/chatgpt-api: Node.js client for the official ChatGPT API. 🔥](https://github.com/transitive-bullshit/chatgpt-api)\n- [terry3041/pyChatGPT: An unofficial Python wrapper for OpenAI's ChatGPT API](https://github.com/terry3041/pyChatGPT)\n- [Sha1rholder/use-ChatGPT-in-GFW: 在中国境内使用 OpenAI 服务的方法](https://github.com/Sha1rholder/use-ChatGPT-in-GFW#%E4%BD%BF%E7%94%A8-openai-api)\n- [ninja/README_zh.md at main · gngpp/ninja](https://github.com/gngpp/ninja/blob/main/README_zh.md)\n\n### Inference\n\n- [Frameworks for Serving LLMs. A comprehensive guide into LLMs inference and serving](https://betterprogramming.pub/frameworks-for-serving-llms-60b7f7b23407)\n- [ggerganov/llama.cpp: Port of Facebook's LLaMA model in C/C++](https://github.com/ggerganov/llama.cpp)\n- [huggingface/text-generation-inference: Large Language Model Text Generation Inference](https://github.com/huggingface/text-generation-inference)\n- [onnxruntime/onnxruntime/python/tools/transformers/notebooks/Inference_GPT2_with_OnnxRuntime_on_CPU](https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/notebooks/Inference_GPT2_with_OnnxRuntime_on_CPU.ipynb)\n- [FasterTransformer/docs/gpt_guide.md at main · NVIDIA/FasterTransformer](https://github.com/NVIDIA/FasterTransformer/blob/main/docs/gpt_guide.md)\n- [OpenNMT/CTranslate2: Fast inference engine for Transformer models](https://github.com/OpenNMT/CTranslate2)\n- [mlc-ai/mlc-llm: Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.](https://github.com/mlc-ai/mlc-llm)\n- [vllm-project/vllm: A high-throughput and memory-efficient inference and serving engine for LLMs](https://github.com/vllm-project/vllm)\n- [sonos/tract: Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference](https://github.com/sonos/tract)\n- [rustformers/llm: An ecosystem of Rust libraries for working with large language models](https://github.com/rustformers/llm)\n- [bentoml/OpenLLM: Operating LLMs in production](https://github.com/bentoml/OpenLLM)\n- [huggingface/candle: Minimalist ML framework for Rust](https://github.com/huggingface/candle)\n- [tairov/llama2.mojo: Inference Llama 2 in one file of pure 🔥](https://github.com/tairov/llama2.mojo)\n\n### Prompts\n\n- [f/awesome-chatgpt-prompts: This repo includes ChatGPT prompt curation to use ChatGPT better.](https://github.com/f/awesome-chatgpt-prompts)\n- [dair-ai/Prompt-Engineering-Guide: 🐙 Guides, papers, lecture, notebooks and resources for prompt engineering](https://github.com/dair-ai/Prompt-Engineering-Guide)\n\n### Parser\n\n- [opendatalab/MinerU: A high-quality tool for convert PDF to Markdown and JSON.一站式开源高质量数据提取工具，将PDF转换成Markdown和JSON格式。](https://github.com/opendatalab/MinerU)\n\n## AI\n\n### Format\n\n- [onnx/onnx: Open standard for machine learning interoperability](https://github.com/onnx/onnx)\n- [huggingface/safetensors: Simple, safe way to store and distribute tensors](https://github.com/huggingface/safetensors)\n- [openai/triton: Development repository for the Triton language and compiler](https://github.com/openai/triton)\n\n### Inference\n\n- [microsoft/onnxruntime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator](https://github.com/microsoft/onnxruntime)\n- [NVIDIA/TensorRT: NVIDIA® TensorRT™, an SDK for high-performance deep learning inference, includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications.](https://github.com/NVIDIA/TensorRT)\n- [openvinotoolkit/openvino: OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference](https://github.com/openvinotoolkit/openvino)\n- [triton-inference-server/server: The Triton Inference Server provides an optimized cloud and edge inferencing solution.](https://github.com/triton-inference-server/server)\n- [KomputeProject/kompute: General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA \u0026 friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.](https://github.com/KomputeProject/kompute)\n- [Adlik/Adlik: Adlik: Toolkit for Accelerating Deep Learning Inference](https://github.com/Adlik/Adlik)\n\n### Deploy\n\n- [tensorflow/serving](https://github.com/tensorflow/serving)\n- [pytorch/serve: Model Serving on PyTorch](https://github.com/pytorch/serve)\n- [TFX: End-to-End Platform for Deploying Production ML Pipelines](https://github.com/tensorflow/tfx)\n- [Kubeflow](https://www.kubeflow.org/)\n- [mvitez/thnets: Basic library that can run networks created with Torch](https://github.com/mvitez/thnets)\n- [Serving Trained Model (aka Model API) - FloydHub Documentation](https://docs.floydhub.com/guides/serving/)\n- [ahkarami/Deep-Learning-in-Production](https://github.com/ahkarami/Deep-Learning-in-Production)\n- [hyperonym/basaran: Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.](https://github.com/hyperonym/basaran)\n\n### Demo/WebAPP\n\n- [holoviz/panel: Panel: The powerful data exploration \u0026 web app framework for Python](https://github.com/holoviz/panel)\n- [rawpython/remi: Python REMote Interface library. Platform independent. In about 100 Kbytes, perfect for your diet.](https://github.com/rawpython/remi)\n- [PySimpleGUI](https://www.pysimplegui.org/en/latest/)\n- [pywebio/PyWebIO: Write interactive web app in script way.](https://github.com/pywebio/PyWebIO)\n- [streamlit/streamlit: Streamlit — A faster way to build and share data apps.](https://github.com/streamlit/streamlit)\n- [gradio-app/gradio: Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!](https://github.com/gradio-app/gradio)\n\n### Toolkit\n\n- [microsoft/hummingbird: Hummingbird compiles trained ML models into tensor computation for faster inference.](https://github.com/microsoft/hummingbird)\n- [PyTorchLightning/pytorch-lightning: The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.](https://github.com/PyTorchLightning/PyTorch-lightning)\n- [Deepfakes](https://github.com/deepfakes/faceswap)\n- [DeepFaceLab](https://github.com/iperov/DeepFaceLab)\n- [Music Generation](https://github.com/salu133445/musegan)\n- [AI_Composer](https://github.com/llSourcell/AI_Composer)\n- [ChatterBot](https://github.com/gunthercox/ChatterBot)\n- [ChatterBot](https://github.com/Decalogue/chat)\n- [GPT2 for Chinese Chitchat](https://github.com/yangjianxin1/GPT2-chitchat)\n- [FaceBook Detectron2: object detection and segmentation.](https://github.com/facebookresearch/detectron2)\n- [Open MMLab Detection Toolbox and Benchmark](https://github.com/open-mmlab/mmdetection)\n- [Open MMLab Computer Vision Foundation](https://github.com/open-mmlab/mmcv)\n- [esdalmaijer/PyGaze: eye tracking experiments](https://github.com/esdalmaijer/PyGaze)\n- [atulapra/Emotion-detection](https://github.com/atulapra/Emotion-detection)\n- [ Multi-Person Pose Estimation\u0026Tracking System](https://github.com/MVIG-SJTU/AlphaPose)\n- [facebookresearch/dlrm: recommendation](https://github.com/facebookresearch/dlrm)\n- [Jupyter Tutorial — Jupyter Tutorial 0.8.0](https://jupyter-tutorial.readthedocs.io/en/latest/)\n\n### Dataset\n\n- [Dataset Search](https://datasetsearch.research.google.com/)\n- [Hugging Face – The AI community building the future.](https://huggingface.co/datasets)\n- [Find Open Datasets and Machine Learning Projects | Kaggle](https://www.kaggle.com/datasets)\n- [Machine Learning Datasets | Papers With Code](https://www.paperswithcode.com/datasets)\n- [Datasets](https://www.reddit.com/r/datasets/)\n- [中文NLP数据集](https://www.cluebenchmarks.com/dataSet_search.html)\n- [Dataset list - A list of the biggest machine learning datasets](https://www.datasetlist.com/)\n- [awesomedata/awesome-public-datasets: A topic-centric list of HQ open datasets.](https://github.com/awesomedata/awesome-public-datasets)\n- [Data Is Plural](https://www.data-is-plural.com/)\n- [OpenML](https://www.openml.org/search?type=data)\n- [InsaneLife/ChineseNLPCorpus: 中文自然语言处理数据集，平时做做实验的材料。欢迎补充提交合并。](https://github.com/InsaneLife/ChineseNLPCorpus)\n\n### DataAnnotation\n\n- [opencv/cvat: Computer Vision Annotation Tool](https://github.com/opencv/cvat)\n- [wkentaro/labelme: Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).](https://github.com/wkentaro/labelme)\n- [generating training data with weak supervision](https://github.com/snorkel-team/snorkel)\n- [make basic image processing operations](https://github.com/jrosebr1/imutils)\n- [nlplab/brat: textual annotation](https://github.com/nlplab/brat)\n- [doccano/doccano: Open source annotation tool for machine learning practitioners.](https://github.com/doccano/doccano)\n- [Overview - CoreNLP](https://stanfordnlp.github.io/CoreNLP/)\n- [HumanSignal/awesome-data-labeling: A curated list of awesome data labeling tools](https://github.com/HumanSignal/awesome-data-labeling)\n\n### DeepLearning\n\n- [fast.ai · Making neural nets uncool again](https://www.fast.ai/)\n- [TensorFlow](https://www.tensorflow.org/tutorials)\n- [Tensorflow Models](https://github.com/tensorflow/models)\n- [DeepLearning Papers](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap)\n- [Awesome-Deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers)\n- [D2L ZH](https://github.com/d2l-ai/d2l-zh)\n- [D2L EN](https://github.com/d2l-ai/d2l-en)\n- [pytorch/tutorials: PyTorch tutorials.](https://github.com/pytorch/tutorials)\n- [Pytorch Examples](https://github.com/pytorch/examples)\n- [Tutorials  |  TensorFlow Core](https://www.tensorflow.org/tutorials)\n- [简单粗暴 TensorFlow 2 | A Concise Handbook of TensorFlow 2 — 简单粗暴 TensorFlow 2 0.4 beta 文档](https://tf.wiki/zh_hans/)\n- [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples)\n- [microsoft: NLP Best Practices \u0026 Examples](https://github.com/microsoft/nlp-recipes)\n- [DeepLearning Projects](https://github.com/aymericdamien/TopDeepLearning)\n- [Ttensorflow_practice](https://github.com/princewen/tensorflow_practice)\n\n\n### MachineLearning\n\n- [microsoft/nni: AutoML toolkit](https://github.com/microsoft/nni)\n- [Imbalanced Learning](https://github.com/scikit-learn-contrib/imbalanced-learn)\n- [Automated Machine Learning tool using genetic programming](https://github.com/EpistasisLab/tpot)\n- [Machine learning evaluation metrics](https://github.com/benhamner/Metrics)\n- [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)\n\n### HyperOptimization\n\n- [autonomio/talos: Hyperparameter Optimization for TensorFlow, Keras and PyTorch](https://github.com/autonomio/talos)\n- [scikit-optimize: sequential model-based optimization in Python — scikit-optimize 0.8.1 documentation](https://scikit-optimize.github.io/stable/)\n- [fmfn/BayesianOptimization: A Python implementation of global optimization with gaussian processes.](https://github.com/fmfn/BayesianOptimization)\n- Ray Tune：[Tune: Scalable Hyperparameter Tuning — Ray v2.0.0.dev0](https://docs.ray.io/en/master/tune/index.html)\n- [hyperopt/hyperopt: Distributed Asynchronous Hyperparameter Optimization in Python](https://github.com/hyperopt/hyperopt)\n- [Introduction to the Keras Tuner  |  TensorFlow Core](https://www.tensorflow.org/tutorials/keras/keras_tuner)\n- Google Vizier：[Vizier 概览  |  AI Platform Vizier  |  Google Cloud](https://cloud.google.com/ai-platform/optimizer/docs/overview)\n- Amazon Sagemaker：[Amazon SageMaker 机器学习模型构建训练部署 - AWS 云服务](https://aws.amazon.com/cn/sagemaker/)\n- Microsoft NNI：[microsoft/nni: An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.](https://github.com/microsoft/nni)\n- Microsoft Advisor：[Optimization advisor overview - Finance \u0026 Operations | Dynamics 365 | Microsoft Docs](https://docs.microsoft.com/en-us/dynamics365/fin-ops-core/dev-itpro/sysadmin/optimization-advisor-overview)\n- [maxpumperla/hyperas: Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization](https://github.com/maxpumperla/hyperas) （已存档）\n- [Avsecz/kopt: Hyper-parameter optimization for Keras](https://github.com/Avsecz/kopt)（一阵没更新了）\n- [rsteca/sklearn-deap: Use evolutionary algorithms instead of gridsearch in scikit-learn](https://github.com/rsteca/sklearn-deap)（一阵没更新了）\n- [HIPS/Spearmint: Spearmint Bayesian optimization codebase](https://github.com/HIPS/Spearmint)（一阵没更新了）\n- [[1603.06560] Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://arxiv.org/abs/1603.06560)\n\n###  TTS\n\n- [coqui-ai/TTS: 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production](https://github.com/coqui-ai/TTS)\n- [RVC-Boss/GPT-SoVITS: 1 min voice data can also be used to train a good TTS model! (few shot voice cloning)](https://github.com/RVC-Boss/GPT-SoVITS/tree/main)\n- [fishaudio/Bert-VITS2: vits2 backbone with multilingual-bert](https://github.com/fishaudio/Bert-VITS2)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhscspring%2Fall4ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhscspring%2Fall4ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhscspring%2Fall4ai/lists"}