{"id":13614027,"url":"https://github.com/bigcash/awesome-ai-list-guide","last_synced_at":"2025-04-13T18:32:03.655Z","repository":{"id":161410263,"uuid":"456769655","full_name":"bigcash/awesome-ai-list-guide","owner":"bigcash","description":"The guide of awesome list about AI","archived":false,"fork":false,"pushed_at":"2024-10-10T07:57:30.000Z","size":132,"stargazers_count":54,"open_issues_count":1,"forks_count":13,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-04-11T13:24:50.657Z","etag":null,"topics":["ai","artificial-intelligence","asr","awesome","awesome-list","cv","deep-learning","machine-learning","nlp","speech","tts"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bigcash.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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}},"created_at":"2022-02-08T03:33:02.000Z","updated_at":"2025-03-14T17:37:13.000Z","dependencies_parsed_at":null,"dependency_job_id":"e3766d93-ceea-4e67-87b1-445344e656c6","html_url":"https://github.com/bigcash/awesome-ai-list-guide","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bigcash%2Fawesome-ai-list-guide","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bigcash%2Fawesome-ai-list-guide/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bigcash%2Fawesome-ai-list-guide/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bigcash%2Fawesome-ai-list-guide/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bigcash","download_url":"https://codeload.github.com/bigcash/awesome-ai-list-guide/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248760446,"owners_count":21157361,"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","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","artificial-intelligence","asr","awesome","awesome-list","cv","deep-learning","machine-learning","nlp","speech","tts"],"created_at":"2024-08-01T20:00:55.964Z","updated_at":"2025-04-13T18:32:03.383Z","avatar_url":"https://github.com/bigcash.png","language":null,"funding_links":[],"categories":["HarmonyOS","Others","Other awesome AI lists","Lists of Lists"],"sub_categories":["Windows Manager"],"readme":"# Awesome AI List Guide\n\nThe guide of awesome list  about **AI**  ( a.k.a., **artificial intelligence**,  **machine learning**, **deep learning**)\n\n **The list is in no particular order!!!**\n\n Pull requests are welcome! \n\n[English](README.md) | [中文](README_ch.md)\n\n# Table of Contents\n\n- [Tutorials](#Tutorials)\n- [CV](#CV)\n- [NLP](#NLP)\n- [Speech](#Speech)\n- [Others](#Others)\n\n\n\n## Tutorials\n\n[awesome-for-beginners](https://github.com/MunGell/awesome-for-beginners):   A list of awesome beginners-friendly projects. \n\n[Awesome production machine learning](https://github.com/EthicalML/awesome-production-machine-learning.git):  A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning \n\n[awesome-ai-infrastructures](https://github.com/1duo/awesome-ai-infrastructures) :   Infrastructures™ for Machine Learning Training/Inference in Production.  \n\n[Production-Level-Deep-Learning](https://github.com/alirezadir/Production-Level-Deep-Learning):  A guideline for building practical production-level deep learning systems to be deployed in real world applications. \n\n[competition_baselines](https://github.com/LogicJake/competition_baselines) : Open competition's baseline\n\n[competition-baseline](https://github.com/datawhalechina/competition-baseline) :  Knowledge, code and ideas of data science competition\n\n[paper-reproduction-tutorials](https://github.com/PaddleEdu/paper-reproduction-tutorials) ： The skill of reproducing papers and sharing PaddlePaddle outstanding projects\n\n[awesome-mlops](https://github.com/visenger/awesome-mlops) :   A curated list of references for MLOps \n\n[awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning) ： A curated list of awesome Machine Learning frameworks, libraries and software. \n\n[Learn-Data-Science-For-Free](https://github.com/therealsreehari/Learn-Data-Science-For-Free) ： This repositary is a combination of different resources lying scattered all over the internet. The reason for making such an repositary is to combine all the valuable resources in a sequential manner, so that it helps every beginners who are in a search of free and structured learning resource for Data Science. For Constant Updates Follow me in … \n\n[best-of-ml-python](https://github.com/ml-tooling/best-of-ml-python) ： 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly. \n\n[build-your-own-x](https://github.com/danistefanovic/build-your-own-x) :  🤓 Build your own (insert technology here) \n\n[tensorflow_practice](https://github.com/princewen/tensorflow_practice) :  Tensorflow practice, including reinforcement learning, recommendation system, NLP, etc \n\n[awesome-courses](https://github.com/prakhar1989/awesome-courses) :  📚 List of awesome university courses for learning Computer Science! \n\n[MT-Reading-List](https://github.com/THUNLP-MT/MT-Reading-List) :  A machine translation reading list maintained by Tsinghua Natural Language Processing Group \n\n[cs-video-courses](https://github.com/Developer-Y/cs-video-courses) :  List of Computer Science courses with video lectures. \n\n[machine-learning-surveys](https://github.com/metrofun/machine-learning-surveys) :  A curated list of Machine Learning Surveys, Tutorials and Books. \n\n[data-science-blogs](https://github.com/rushter/data-science-blogs) :  A curated list of data science blogs \n\n[awesome-tensorflow](https://github.com/jtoy/awesome-tensorflow) :  TensorFlow - A curated list of dedicated resources \n\n[ds-cheatsheets](https://github.com/FavioVazquez/ds-cheatsheets) :  List of Data Science Cheatsheets to rule the world \n\n[awesome-R](https://github.com/qinwf/awesome-R) :  A curated list of awesome R packages, frameworks and software. \n\n[awesome-youtubers](https://github.com/JoseDeFreitas/awesome-youtubers) :  ▶️ An awesome list of awesome YouTubers that teach about technology. Tutorials about web development, computer science, machine learning, game development, cybersecurity, and more. \n\n[Book_List](https://github.com/mukeshmithrakumar/Book_List) :  Python, Machine Learning, Deep Learning and Data Science Books \n\n[awesome-deep-learning](https://github.com/ChristosChristofidis/awesome-deep-learning) :  A curated list of awesome Deep Learning tutorials, projects and communities. \n\n[awesome-artificial-intelligence](https://github.com/owainlewis/awesome-artificial-intelligence) :  A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers. \n\n[awesome-project-ideas](https://github.com/NirantK/awesome-project-ideas) :  Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas \n\n[awesome-datascience](https://github.com/academic/awesome-datascience) :  📝 An awesome Data Science repository to learn and apply for real world problems. \n\n[awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers) :  The most cited deep learning papers \n\n[awesome-machine-learning-cn](https://github.com/jobbole/awesome-machine-learning-cn) : Machine learning resources of Chinese version, including the framework, library and software in the field of machine learning\n\n[Awesome-PyTorch-Chinese](https://github.com/INTERMT/Awesome-PyTorch-Chinese) :  the most complete pytorch learning resources in history\n\n[awesome-AI-books](https://github.com/zslucky/awesome-AI-books) :  Some awesome AI related books and pdfs for learning and downloading, also apply some playground models for learning \n\n[awesome-ml-courses](https://github.com/luspr/awesome-ml-courses) :  Awesome free machine learning and AI courses with video lectures. \n\n[awesome-ai-ml-dl](https://github.com/neomatrix369/awesome-ai-ml-dl) :  Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics. \n\n[Awesome-Noah](https://github.com/AI-Sphere/Awesome-Noah) :   Awesome Top Solution List of Excellent AI Competitions \n\n[my-awesome-AI-bookmarks](https://github.com/goodrahstar/my-awesome-AI-bookmarks) :  Curated list of my reads, implementations and core concepts of Artificial Intelligence, Deep Learning, Machine Learning by best folk in the world. \n\n[awesome-DeepLearning](https://github.com/PaddlePaddle/awesome-DeepLearning) :  The course, case and knowledge of Deep Learning and AI \n\n[Machine-Learning-Collection](https://github.com/aladdinpersson/Machine-Learning-Collection) :  A resource for learning about Machine learning \u0026 Deep Learning \n\n[DeepLearningSystem](https://github.com/chenzomi12/DeepLearningSystem) :  Deep Learning System core principles introduction. \n\n[free-programming-books](https://github.com/EbookFoundation/free-programming-books) :  📚 Freely available programming books \n\n[research-method](https://github.com/secdr/research-method) :  Paper Writing and Resources Sharing\n\n\n\n## CV\n\n[awesome-hand-pose-estimation](https://github.com/xinghaochen/awesome-hand-pose-estimation):  Awesome work on hand pose estimation/tracking \n\n[CV-Backbones](https://github.com/huawei-noah/CV-Backbones) :  CV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.  \n\n[SceneTextPapers](https://github.com/Jyouhou/SceneTextPapers.git):   Tracking the latest progress in Scene Text Detection and Recognition: Must-read papers well organized \n\n[Awesome-GANs](https://github.com/kozistr/Awesome-GANs) :  Awesome Generative Adversarial Networks with tensorflow \n\n[OCR_DataSet](https://github.com/WenmuZhou/OCR_DataSet) :  Collect and sort out the data set related to OCR and unify the annotation format for the needs of the experiment \n\n[awesome-ocr](https://github.com/wanghaisheng/awesome-ocr) :  A curated list of promising OCR resources \n\n[Awesome-Table-Recognition](https://github.com/cv-small-snails/Awesome-Table-Recognition) :  A curated list of resources dedicated to table recognition \n\n[awesome-object-detection](https://github.com/amusi/awesome-object-detection) :  Awesome Object Detection based on handong1587 github \n\n[deep_learning_object_detection](https://github.com/hoya012/deep_learning_object_detection) :  A paper list of object detection using deep learning. \n\n[awesome-captcha](https://github.com/ZYSzys/awesome-captcha) :  🔑 Curated list of awesome captcha libraries and crack tools.\n\n[image-to-image-papers](https://github.com/lzhbrian/image-to-image-papers) :  🦓\u003c-\u003e🦒 🌃\u003c-\u003e🌆 A collection of image to image papers with code (constantly updating) \n\n[Deep-Learning-Papers-Reading-Roadmap](https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap) :  Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech! \n\n[benchmark_results](https://github.com/foolwood/benchmark_results) :  Visual Tracking Paper List \n\n[awesome_3DReconstruction_list](https://github.com/openMVG/awesome_3DReconstruction_list) :  A curated list of papers \u0026 resources linked to 3D reconstruction from images. \n\n[the-gan-zoo](https://github.com/hindupuravinash/the-gan-zoo) :  A list of all named GANs! \n\n[awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision) :  A curated list of awesome computer vision resources \n\n[multi-object-tracking-paper-list](https://github.com/SpyderXu/multi-object-tracking-paper-list) :  Paper list and source code for multi-object-tracking \n\n[awesome-deep-vision](https://github.com/kjw0612/awesome-deep-vision) :  A curated list of deep learning resources for computer vision \n\n[AdversarialNetsPapers](https://github.com/zhangqianhui/AdversarialNetsPapers) :  Awesome paper list with code about generative adversarial nets (gan)\n\n[awesome-lane-detection](https://github.com/amusi/awesome-lane-detection) :  A paper list of lane detection. \n\n[Paper_Reading_List](https://github.com/ArcherFMY/Paper_Reading_List) :   Recommended Papers. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Learning (cs.LG) \n\n[awesome-network-embedding](https://github.com/chihming/awesome-network-embedding) :  A curated list of network embedding techniques. \n\n[gans-awesome-applications](https://github.com/nashory/gans-awesome-applications) :  Curated list of awesome GAN applications and demo \n\n[WeakSupervisedSegmentationList](https://github.com/JackieZhangdx/WeakSupervisedSegmentationList) :  This repository contains lists of state-or-art weakly supervised semantic segmentation works \n\n[awesome-action-recognition](https://github.com/jinwchoi/awesome-action-recognition) :  A curated list of action recognition and related area resources \n\n[really-awesome-gan](https://github.com/nightrome/really-awesome-gan) : A list of papers on Generative Adversarial (Neural) Networks \n\n[awesome-panoptic-segmentation](https://github.com/Angzz/awesome-panoptic-segmentation) :  Panoptic Segmentation Resources List \n\n[Pedestrian-Attribute-Recognition-Paper-List](https://github.com/wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List) :  Paper list on Pedestrian Attribute Recognition (PAR) and related tasks (Pattern Recognition 2021) \n\n[awesome-vqa](https://github.com/chingyaoc/awesome-vqa) :  Visual Q\u0026A reading list \n\n[3D-Shape-Analysis-Paper-List](https://github.com/yinyunie/3D-Shape-Analysis-Paper-List) :  A list of recent papers, libraries and datasets about 3D shape/scene analysis (by topics, updating). \n\n[awesome-semantic-segmentation](https://github.com/mrgloom/awesome-semantic-segmentation) : awesome-semantic-segmentation\n\n[Awesome-Crowd-Counting](https://github.com/gjy3035/Awesome-Crowd-Counting) :  Awesome Crowd Counting \n\n[awesome-Face_Recognition](https://github.com/ChanChiChoi/awesome-Face_Recognition) :  papers about Face Detection; Face Alignment; Face Recognition \u0026\u0026 Face Identification \u0026\u0026 Face Verification \u0026\u0026 Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution \u0026\u0026 Face Deblurring; Face Generation \u0026\u0026 Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval; \n\n[AWESOME-FER](https://github.com/EvelynFan/AWESOME-FER) :  Top conferences \u0026 Journals focused on Facial expression recognition (FER)/ Facial action unit (FAU)  \n\n[Awesome-Gaze-Estimation](https://github.com/cvlab-uob/Awesome-Gaze-Estimation) :  Awesome Curated List of Eye Gaze Estimation Paper \n\n[awesome-ai-art-image-synthesis](https://github.com/altryne/awesome-ai-art-image-synthesis) :  A list of awesome tools, ideas, prompt engineering tools, colabs, models, and helpers for the prompt designer playing with aiArt and image synthesis. Covers Dalle2, MidJourney, StableDiffusion, and open source tools. \n\n[Diffusion-Models-Papers-Survey-Taxonomy](https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy) :  Diffusion model papers, survey, and taxonomy \n\n[A-Survey-on-Generative-Diffusion-Model](https://github.com/chq1155/A-Survey-on-Generative-Diffusion-Model) :  A curated list for diffusion generative models introduced by the paper \n\n[Awesome-Face-Restoration](https://github.com/TaoWangzj/Awesome-Face-Restoration) :  A comprehensive list of recources (papers, repositories etc.) about face restoration methods. \n\n[awesome-point-cloud-analysis](https://github.com/Yochengliu/awesome-point-cloud-analysis) :  A list of papers and datasets about point cloud analysis (processing) \n\n[awesome-ai-painting](https://github.com/hua1995116/awesome-ai-painting) :  stable diffusion tutorial、disco diffusion tutorial、 AI Platform \n\n[awesome-aigc](https://github.com/gongminmin/awesome-aigc) :  A list of awesome AIGC works \n\n[awesome-llm-and-aigc](https://github.com/codingonion/awesome-llm-and-aigc) :  A collection of some awesome public projects about Large Language Model, Vision Foundation Model and AI Generated Content. \n\n[awesome-text-to-video](https://github.com/jianzhnie/awesome-text-to-video) :  A Survey on Text-to-Video Generation/Synthesis. \n\n[Awesome-AIGC-Tutorials](https://github.com/luban-agi/Awesome-AIGC-Tutorials) :  Curated tutorials and resources for Large Language Models, AI Painting, and more. \n\n[Awesome-AIGC](https://github.com/wshzd/Awesome-AIGC) :  AIGC资料汇总学习，持续更新...... \n\n\n\n## NLP\n\n\n\n[nlp-tutorial](https://github.com/graykode/nlp-tutorial) :  Natural Language Processing Tutorial for Deep Learning Researchers \n\n[language-resources](https://github.com/google/language-resources) :  Datasets and tools for basic natural language processing. \n\n[Summarization-Papers](https://github.com/xcfcode/Summarization-Papers) ： Summarization Papers \n\n[CLUEDatasetSearch](https://github.com/CLUEbenchmark/CLUEDatasetSearch) ： Search all Chinese NLP datasets, with common English NLP datasets\n\n[nlpdemo-ch-wordlib](https://github.com/MrLi008/nlpdemo-ch-wordlib) :  Chinese Thesaurus\n\n[ChineseNLP](https://github.com/didi/ChineseNLP) :  Datasets, SOTA results of every fields of Chinese NLP\n\n[ChineseNLPCorpus](https://github.com/InsaneLife/ChineseNLPCorpus) :  Chinese natural language processing data set is the material for experiments at ordinary times. 中文自然语言处理数据集\n\n[Chinese-Word-Vectors](https://github.com/Embedding/Chinese-Word-Vectors) :  100+ Chinese Word Vectors 上百种预训练中文词向量\n\n[ChineseNlpCorpus](https://github.com/SophonPlus/ChineseNlpCorpus) :  Collect, organize and publish Chinese natural language processing corpus / data set\n\n[nlp-competitions-list-review](https://github.com/zhpmatrix/nlp-competitions-list-review) :  Resume the top plan of all NLP competitions, only focus on NLP competitions, and keep updating! NLP比赛top方案\n\n[funNLP](https://github.com/fighting41love/funNLP) :  Chinese and English sensitive words, language detection, Chinese and foreign mobile phone / telephone home / operator query, name inference, gender, mobile phone number extraction, ID card extraction, email extraction, and more ... 有趣的中文NLP\n\n[nlp_chinese_corpus](https://github.com/brightmart/nlp_chinese_corpus) :  大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP \n\n[awesome-2vec](https://github.com/MaxwellRebo/awesome-2vec) :  Curated list of 2vec-type embedding models \n\n[chatbot-list](https://github.com/lizhe2004/chatbot-list) :  Share and introduce the application, architecture and algorithm of intelligent customer service and chat robot in the industry   行业内关于智能客服、聊天机器人的应用和架构、算法分享和介绍 \n\n[awesome-chatbot-list](https://github.com/aceimnorstuvwxz/awesome-chatbot-list) :  深度学习聊天机器人资源集合 Awesome chatbot resource list \n\n[nmt-list](https://github.com/jonsafari/nmt-list) :  A list of Neural MT implementations \n\n[Question-Generation-Paper-List](https://github.com/teacherpeterpan/Question-Generation-Paper-List) :  A summary of must-read papers for Neural Question Generation (NQG) \n\n[awesome-nlp](https://github.com/keon/awesome-nlp) :  📖 A curated list of resources dedicated to Natural Language Processing (NLP) \n\n[Style-Transfer-in-Text](https://github.com/fuzhenxin/Style-Transfer-in-Text) :  Paper List for Style Transfer in Text \n\n[TG-Reading-List](https://github.com/THUNLP-MT/TG-Reading-List) :  A text generation reading list maintained by Tsinghua Natural Language Processing Group. \n\n[awesome-sentence-embedding](https://github.com/Separius/awesome-sentence-embedding) :  A curated list of pretrained sentence and word embedding models \n\n[Awesome-Chinese-NLP](https://github.com/crownpku/Awesome-Chinese-NLP) :  A curated list of resources for Chinese NLP 中文自然语言处理相关资料 \n\n[*awesome*_Chinese_medical_*NLP*](https://github.com/GanjinZero/awesome_Chinese_medical_NLP) :  Arrangement of Chinese medical NLP public resources 中文医学NLP公开资源整理：术语集/语料库/词向量/预训练模型/知识图谱/命名实体识别/QA/信息抽取/模型/论文/etc \n\n[awesome-dl4nlp](https://github.com/brianspiering/awesome-dl4nlp) :  A curated list of awesome Deep Learning for Natural Language Processing resources \n\n[Awesome-Korean-NLP](https://github.com/datanada/Awesome-Korean-NLP) :  A curated list of resources for NLP (Natural Language Processing) for Korean \n\n[awesome-bert-nlp](https://github.com/cedrickchee/awesome-bert-nlp) :  A curated list of NLP resources focused on BERT, attention mechanism, Transformer networks, and transfer learning. \n\n[awesome-knowledge-graph](https://github.com/husthuke/awesome-knowledge-graph) :  a cute list of Knowledge graph\n\n[Task-Oriented-Dialogue-Research-Progress-Survey](https://github.com/AtmaHou/Task-Oriented-Dialogue-Research-Progress-Survey) :  A datasets and methods survey about task-oriented dialogue, including recent datasets and SOTA leaderboards. \n\n[Text_Classification](https://github.com/kk7nc/Text_Classification) : Text Classification Algorithms: A Survey\n\n[awesome-punctuator](https://github.com/bigcash/awesome-punctuator) :  A curated list of awesome punctuator \n\n[text-classification-surveys](https://github.com/xiaoqian19940510/text-classification-surveys) :  文本分类资源汇总，包括深度学习文本分类模型 \n\n[awesome_Chinese_medical_NLP](https://github.com/GanjinZero/awesome_Chinese_medical_NLP) :  中文医学NLP公开资源整理：术语集/语料库/词向量/预训练模型/知识图谱/命名实体识别/QA/信息抽取/模型/论文/etc \n\n[Awesome-LLM](https://github.com/Hannibal046/Awesome-LLM) :  Awesome-LLM: a curated list of Large Language Model \n\n[Prompt-Engineering-Guide](https://github.com/dair-ai/Prompt-Engineering-Guide) :  Guides, papers, lecture, and resources for prompt engineering \n\n[PromptPapers](https://github.com/thunlp/PromptPapers) :  Must-read papers on prompt-based tuning for pre-trained language models. \n\n[awesome-chatgpt-prompts](https://github.com/f/awesome-chatgpt-prompts) :  This repo includes ChatGPT prompt curation to use ChatGPT better. \n\n[awesome-chatgpt-prompts-zh](https://github.com/PlexPt/awesome-chatgpt-prompts-zh) :  ChatGPT 中文调教指南。各种场景使用指南。学习怎么让它听你的话。 \n\n[awesome-gpt3](https://github.com/elyase/awesome-gpt3) :  Awesome GPT-3 is a collection of demos and articles about the [OpenAI GPT-3 API](https://openai.com/blog/openai-api/). \n\n[Awesome-ChatGPT](https://github.com/dalinvip/Awesome-ChatGPT) :  ChatGPT资料汇总学习，持续更新...... \n\n[awesome-open-gpt](https://github.com/EwingYangs/awesome-open-gpt) :  Collection of Open Source Projects Related to GPT，GPT相关开源项目合集🚀、精选🔥🔥 \n\n[awesome-chatgpt](https://github.com/humanloop/awesome-chatgpt) :  Curated list of awesome tools, demos, docs for ChatGPT and GPT-3 \n\n[awesome-gpt4](https://github.com/radi-cho/awesome-gpt4) :  A curated list of prompts, tools, and resources regarding the GPT-4 language model. \n\n[Awesome-ChatGPT](https://github.com/runningcheese/Awesome-ChatGPT) :  ChatGPT related knowledge and resource\n\n[awesome-gpt](https://github.com/formulahendry/awesome-gpt) :  A curated list of awesome projects and resources related to GPT, ChatGPT, OpenAI, LLM, and more. \n\n[awesome-instruction-dataset](https://github.com/yaodongC/awesome-instruction-dataset) :  A collection of open-source dataset to train instruction-following LLMs (ChatGPT,LLaMA,Alpaca) \n\n[LLMSurvey](https://github.com/RUCAIBox/LLMSurvey) :  The official GitHub page for the survey paper \"A Survey of Large Language Models\". \n\n[awesome-langchain](https://github.com/kyrolabs/awesome-langchain) :  Awesome list of tools and projects with the awesome LangChain framework \n[awesome-pretrained-chinese-nlp-models](https://github.com/lonePatient/awesome-pretrained-chinese-nlp-models) :   Awesome Pretrained Chinese NLP Models  \n\n[Awesome-Multimodal-Large-Language-Models](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) :  Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation. \n\n[awesome-LLMs-In-China](https://github.com/wgwang/awesome-LLMs-In-China) : LLMs in china\n\n[awesome-open-foundation-models](https://github.com/wgwang/awesome-open-foundation-models) :  Open foundation models, such LLama2, ChatGLM, etc. \n\n[awesome-LLM-benchmarks](https://github.com/wgwang/awesome-LLM-benchmarks) :  Awesome LLM Benchmarks to evaluate the LLMs across text, code, image, audio, video and more. \n\n[Awesome-Chinese-LLM](https://github.com/HqWu-HITCS/Awesome-Chinese-LLM) :  整理开源的中文大语言模型，以规模较小、可私有化部署、训练成本较低的模型为主，包括底座模型，垂直领域微调及应用，数据集与教程等。 \n\n[Awesome-Domain-LLM](https://github.com/luban-agi/Awesome-Domain-LLM) :  收集和梳理垂直领域的开源模型、数据集及评测基准。 \n\n[DecryptPrompt](https://github.com/DSXiangLi/DecryptPrompt) :  总结Prompt\u0026LLM论文，开源数据\u0026模型，AIGC应用 \n\n[Awesome-Open-domain-Dialogue-Models](https://github.com/cingtiye/Awesome-Open-domain-Dialogue-Models) :  Awesome Open-domain Dialogue Models，高质量开放域对话模型集合 \n\n[LLMDataHub](https://github.com/Zjh-819/LLMDataHub) :  A quick guide (especially) for trending instruction finetuning datasets \n\n[NLPer-Arsenal](https://github.com/TingFree/NLPer-Arsenal) :  收录NLP竞赛策略实现、各任务baseline、相关竞赛经验贴（当前赛事、往期赛事、训练赛）、NLP会议时间、常用自媒体、GPU推荐等 \n\n[awesome-llm-apps](https://github.com/Shubhamsaboo/awesome-llm-apps) :  Collection of awesome LLM apps with RAG using OpenAI, Anthropic, Gemini and opensource models. \n\n[llm-app](https://github.com/pathwaycom/llm-app) :  Dynamic RAG for enterprise. Ready to run with Docker,⚡in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more. \n\n[Awesome-LLM-RAG-Application](https://github.com/lizhe2004/Awesome-LLM-RAG-Application) :  the resources about the application based on LLM with RAG pattern \n\n[Awesome-Text2SQL](https://github.com/eosphoros-ai/Awesome-Text2SQL) :  Curated tutorials and resources for Large Language Models, Text2SQL, Text2DSL、Text2API、Text2Vis and more. \n\n[NL2SQL](https://github.com/yechens/NL2SQL) :  Text2SQL 语义解析数据集、解决方案、paper资源整合项目 \n\n[awesome-chatgpt-dataset](https://github.com/voidful/awesome-chatgpt-dataset) :  Unlock the Power of LLM: Explore These Datasets to Train Your Own ChatGPT! \n\n[Awesome-LLM-Reasoning](https://github.com/atfortes/Awesome-LLM-Reasoning) :  Reasoning in Large Language Models: Papers and Resources, including Chain-of-Thought and OpenAI o1 🍓 \n\n[Awesome-LLM-Strawberry](https://github.com/hijkzzz/Awesome-LLM-Strawberry) :  A collection of LLM papers, blogs, and projects, with a focus on OpenAI o1 and reasoning techniques. \n\n[Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) :  An awesome \u0026 curated list of best LLMOps tools for developers \n\n[awesome-LLM-resourses](https://github.com/WangRongsheng/awesome-LLM-resourses) :  🧑‍🚀 全世界最好的LLM资料总结 | Summary of the world's best LLM resources. \n\n[Awesome-LLM-Robotics](https://github.com/GT-RIPL/Awesome-LLM-Robotics) :  A comprehensive list of papers using large language/multi-modal models for Robotics/RL, including papers, codes, and related websites \n\n[Awesome-LLM-Inference](https://github.com/DefTruth/Awesome-LLM-Inference) :  📖A curated list of Awesome LLM Inference Paper with codes, TensorRT-LLM, vLLM, streaming-llm, AWQ, SmoothQuant, WINT8/4, Continuous Batching, FlashAttention, PagedAttention etc. \n\n[Awesome-LLM-RAG](https://github.com/jxzhangjhu/Awesome-LLM-RAG) :  Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models \n\n[Awesome-LLM-Compression](https://github.com/HuangOwen/Awesome-LLM-Compression) :  Awesome LLM compression research papers and tools. \n\n[Awesome-LLMs-on-device](https://github.com/NexaAI/Awesome-LLMs-on-device) :  Awesome LLMs on Device: A Comprehensive Survey \n\n[Awesome-LLMs-Datasets](https://github.com/lmmlzn/Awesome-LLMs-Datasets) :  Summarize existing representative LLMs text datasets. \n\n[awesome-ml](https://github.com/underlines/awesome-ml) :  Curated list of useful LLM / Analytics / Datascience resources \n\n[awesome-llm-security](https://github.com/corca-ai/awesome-llm-security) :  A curation of awesome tools, documents and projects about LLM Security. \n\n[Awesome-LLM4AD](https://github.com/Thinklab-SJTU/Awesome-LLM4AD) :  A curated list of awesome LLM for Autonomous Driving resources\n\n[awesome-llm-powered-agent](https://github.com/hyp1231/awesome-llm-powered-agent) :  Awesome things about LLM-powered agents. Papers / Repos / Blogs \n\n[awesome-llm-json](https://github.com/imaurer/awesome-llm-json) :  Resource list for generating JSON using LLMs via function calling, tools, CFG. Libraries, Models, Notebooks, etc. \n\n[Awesome-Code-LLM](https://github.com/codefuse-ai/Awesome-Code-LLM) :  A curated list of language modeling researches for code and related datasets. \n\n[awesome-llm-interpretability](https://github.com/JShollaj/awesome-llm-interpretability) :  A curated list of Large Language Model (LLM) Interpretability resources. \n\n[Awesome-Graph-LLM](https://github.com/XiaoxinHe/Awesome-Graph-LLM) :  A collection of AWESOME things about Graph-Related LLMs. \n\n[Awesome-GPT-Agents](https://github.com/fr0gger/Awesome-GPT-Agents) :  A curated list of GPT agents for cybersecurity \n\n[LLM4Rec-Awesome-Papers](https://github.com/WLiK/LLM4Rec-Awesome-Papers) :  A list of awesome papers and resources of recommender system on large language model (LLM). \n\n[Awesome-LLM-KG](https://github.com/RManLuo/Awesome-LLM-KG) :  Awesome papers about unifying LLMs and KGs \n\n[awesome_LLMs_interview_notes](https://github.com/jackaduma/awesome_LLMs_interview_notes) :  LLMs interview notes and answers:该仓库主要记录大模型（LLMs）算法工程师相关的面试题和参考答案 \n\n\n\n## Speech\n\n[awesome-speech-recognition-speech-synthesis-papers](https://github.com/zzw922cn/awesome-speech-recognition-speech-synthesis-papers) ： Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC) \n\n [Speech-Separation-Paper-Tutorial](https://github.com/JusperLee/Speech-Separation-Paper-Tutorial) ： A must-read paper for speech separation based on neural networks \n\n[speech-recognition-papers](https://github.com/wenet-e2e/speech-recognition-papers):  Towards hot directions in industrial end to end speech recognition \n\n[awesome-data-augmentation](https://github.com/CrazyVertigo/awesome-data-augmentation)![](https://img.shields.io/github/stars/CrazyVertigo/awesome-data-augmentation.svg?style=social)：  This is a list of awesome methods about data augmentation. \n\n[Awesome-SLP](https://github.com/BenSaunders27/Awesome-SLP.git):   A curated list of awesome work on Sign Language Production \n\n [Awesome-SLU-Survey](https://github.com/yizhen20133868/Awesome-SLU-Survey) ： Tracking the progress in SLU (resources, code, and new frontiers etc.) \n\n[speech_dataset](https://github.com/double22a/speech_dataset):  The dataset of Speech Recognition \n\n[awesome_OpenSetRecognition_list](https://github.com/iCGY96/awesome_OpenSetRecognition_list):  A curated list of papers \u0026 resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition \n\n[speech-synthesis-paper](https://github.com/wenet-e2e/speech-synthesis-paper) :  List of speech synthesis papers. \n\n[Awesome-Speech-Enhancement](https://github.com/nanahou/Awesome-Speech-Enhancement) ： A tutorial for Speech Enhancement researchers and practitioners. The purpose of this repo is to organize the world’s resources for speech enhancement and make them universally accessible and useful. \n\n[awesome-speech-enhancement](https://github.com/WenzheLiu-Speech/awesome-speech-enhancement) :  speech enhancement\\speech seperation\\sound source localization \n\n[speech_data_augment](https://github.com/zzpDapeng/speech_data_augment) :  A summary of speech data augment algorithms \n\n[wer_are_we](https://github.com/syhw/wer_are_we) :  Attempt at tracking states of the arts and recent results (bibliography) on speech recognition. \n\n[awesome-diarization](https://github.com/wq2012/awesome-diarization) :  A curated list of awesome Speaker Diarization papers, libraries, datasets, and other resources. \n\n[awesome-audio-visualization](https://github.com/willianjusten/awesome-audio-visualization) :  A curated list about Audio Visualization. \n\n[awesome-deep-learning-music](https://github.com/ybayle/awesome-deep-learning-music) :  List of articles related to deep learning applied to music \n\n[speech-language-processing](https://github.com/edobashira/speech-language-processing) :  A curated list of speech and natural language processing resources \n\n[SpeechAlgorithms](https://github.com/Ryuk17/SpeechAlgorithms) :  Speech Algorithms Collections \n\n[awesome-vad](https://github.com/bigcash/awesome-vad) : A curated list of awesome voice activity detection \n\n[SER-datasets](https://github.com/SuperKogito/SER-datasets) :  A collection of datasets for the purpose of emotion recognition/detection in speech. \n\n[awesome-openai-whisper](https://github.com/ancs21/awesome-openai-whisper) :  A curated list of awesome OpenAI's Whisper \n\n[Tutorial_Separation](https://github.com/gemengtju/Tutorial_Separation) :  This repo summarizes the tutorials, datasets, papers, codes and tools for speech separation and speaker extraction task. You are kindly invited to pull requests. \n\n[open-speech-corpora](https://github.com/coqui-ai/open-speech-corpora) :  💎 A list of accessible speech corpora for ASR, TTS, and other Speech Technologies\n\n[awesome-vits](https://github.com/34j/awesome-vits) :  List of repositories relevant to VITS.  \n\n[Awesome-Text-to-Speech-TTS](https://github.com/TouchSky-Lab/Awesome-Text-to-Speech-TTS) :  Awesome TTS \n\n[awesome-tts-samples](https://github.com/seungwonpark/awesome-tts-samples) :  Awesome list of TTS papers with audio samples \n\n[awesome-disfluency-detection](https://github.com/pariajm/awesome-disfluency-detection) :  A curated list of awesome disfluency detection publications along with the released code and bibliographical information \n\n[WeDataset](https://github.com/wenet-e2e/wenet/issues/2094): List of (OpenSource data) + (Crawler Resources)\n\n\n\n## Others\n\n[anomaly-detection-resources](https://github.com/yzhao062/anomaly-detection-resources) :  Anomaly detection related books, papers, videos, and toolboxes \n\n[awesome-anomaly-detection](https://github.com/hoya012/awesome-anomaly-detection) :  A curated list of awesome anomaly detection resources \n\n[Surface-Defect-Detection](https://github.com/Charmve/Surface-Defect-Detection) :   Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance. \n\n[Awesome-Meta-Learning](https://github.com/sudharsan13296/Awesome-Meta-Learning) :  A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources. \n\n[GitHub-Chinese-Top-Charts](https://github.com/GrowingGit/GitHub-Chinese-Top-Charts) :  🇨🇳 GitHub chinese top list 中文排行榜，各语言分离设置「软件 / 资料」榜单，精准定位中文好项目。各取所需，互不干扰，高效学习。\n\n[state-of-the-art-result-for-machine-learning-problems](https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems) :  This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). \n\n[PyTorchTricks](https://github.com/lartpang/PyTorchTricks) :  Some tricks of pytorch... ⭐ \n\n[Awesome-pytorch-list-CNVersion](https://github.com/xavier-zy/Awesome-pytorch-list-CNVersion) :  Awesome-pytorch-list 翻译工作进行中...... \n\n[Awesome-pytorch-list](https://github.com/bharathgs/Awesome-pytorch-list) :  A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc. \n\n[deeplearning-models](https://github.com/rasbt/deeplearning-models) :  A collection of various deep learning architectures, models, and tips \n\n[awesome-data-labeling](https://github.com/heartexlabs/awesome-data-labeling) :  A curated list of awesome data labeling tools \n\n[Awesome-Learning-with-Label-Noise](https://github.com/subeeshvasu/Awesome-Learning-with-Label-Noise) :  A curated list of resources for Learning with Noisy Labels \n\n[awesome-music-production](https://github.com/ad-si/awesome-music-production.git):   A curated list of software, services and resources to create and distribute music.  \n\n[leetcode-master](https://github.com/youngyangyang04/leetcode-master) ： LeetCode Introduction 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀 \n\n[awesome-python-login-model](https://github.com/Kr1s77/awesome-python-login-model) ： 😮python crawlers 模拟登陆一些大型网站，还有一些简单的爬虫， \n\n[awesome-spider](https://github.com/facert/awesome-spider) :   crawlers list 爬虫集合 \n\n[awesome-python](https://github.com/vinta/awesome-python) :  A curated list of awesome Python frameworks, libraries, software and resources \n\n[awesome-remote-job](https://github.com/lukasz-madon/awesome-remote-job) :  A curated list of awesome remote jobs and resources. \n\n[public-apis](https://github.com/public-apis/public-apis) :  A collective list of free APIs \n\n[Public-APIs](https://github.com/n0shake/Public-APIs) :  📚 A public list of APIs from round the web. \n\n[public-api-lists](https://github.com/public-api-lists/public-api-lists) :  A collective list of free APIs for use in software and web development 🚀 \n\n[lists](https://github.com/jnv/lists) :  The definitive list of lists (of lists) curated on GitHub and elsewhere \n\n[interview](https://github.com/Olshansk/interview) :  Everything you need to prepare for your technical interview \n\n[A-to-Z-Resources-for-Students](https://github.com/dipakkr/A-to-Z-Resources-for-Students) :  ✅ Curated list of resources for college students \n\n[awesome-math](https://github.com/rossant/awesome-math) :  A curated list of awesome mathematics resources \n\n[awesome-raspberry-pi](https://github.com/thibmaek/awesome-raspberry-pi) :  📝 A curated list of awesome Raspberry Pi tools, projects, images and resources \n\n[science-based-games-list](https://github.com/stared/science-based-games-list) :  Science-based games - a collaborative list \n\n[awesome-SLAM-list](https://github.com/OpenSLAM/awesome-SLAM-list) :  awesome-SLAM-list \n\n[awesome-slam](https://github.com/kanster/awesome-slam) :  A curated list of awesome SLAM tutorials, projects and communities. \n\n[SLAM-All-In-One](https://github.com/zhouyong1234/SLAM-All-In-One) :  SLAM汇总，包括多传感器融合建图、定位、VIO系列、常用工具包、开源代码注释和公式推导、文章综述 \n\n[awesome-robotics](https://github.com/kiloreux/awesome-robotics) :  A list of awesome Robotics resources \n\n[TopDeepLearning](https://github.com/aymericdamien/TopDeepLearning) :  A list of popular github projects related to deep learning \n\n[awesome-ai-residency](https://github.com/dangkhoasdc/awesome-ai-residency) :  List of AI Residency Programs \n\n[ICRA2020-paper-list](https://github.com/PaoPaoRobot/ICRA2020-paper-list) :  ICRA2020 paperlist by paopaorobot,  ICRA 2020 : the 2020 IEEE International Conference on Robotics and Automation. \n\n[RSPapers](https://github.com/hongleizhang/RSPapers) :  A Curated List of Must-read Papers on Recommender System. \n\n[Awesome-Embedded](https://github.com/nhivp/Awesome-Embedded) :  A curated list of awesome embedded programming. \n\n[useful-computer-vision-phd-resources](https://github.com/hassony2/useful-computer-vision-phd-resources) :  Lists of resources useful for my PhD in computer vision \n\n[spatio-temporal-paper-list](https://github.com/Eilene/spatio-temporal-paper-list) :  Spatio-temporal modeling 论文列表（主要是graph convolution相关) \n\n[awesome-self-supervised-learning](https://github.com/jason718/awesome-self-supervised-learning) :  A curated list of awesome self-supervised methods \n\n[medical-imaging-datasets](https://github.com/sfikas/medical-imaging-datasets) :  A list of Medical imaging datasets. \n\n[awesome-roadmaps](https://github.com/liuchong/awesome-roadmaps) :  A curated list of roadmaps. \n\n[EEG-Datasets](https://github.com/meagmohit/EEG-Datasets) :  A list of all public EEG-datasets \n\n[MARL-Papers](https://github.com/LantaoYu/MARL-Papers) :  Paper list of multi-agent reinforcement learning (MARL) \n\n[awesome-multimodal-ml](https://github.com/pliang279/awesome-multimodal-ml) :  Reading list for research topics in multimodal machine learning \n\n[Awesome-Multimodal-Research](https://github.com/Eurus-Holmes/Awesome-Multimodal-Research) :  A curated list of Multimodal Related Research. \n\n[deep-reinforcement-learning-papers](https://github.com/junhyukoh/deep-reinforcement-learning-papers) :  A list of recent papers regarding deep reinforcement learning \n\n[awesome-machine-learning-interpretability](https://github.com/jphall663/awesome-machine-learning-interpretability) :  A curated list of awesome machine learning interpretability resources. \n\n[awesome-fast-attention](https://github.com/Separius/awesome-fast-attention) :  list of efficient attention modules \n\n[deeplearning-biology](https://github.com/hussius/deeplearning-biology) :  A list of deep learning implementations in biology \n\n[FreeML](https://github.com/Shujian2015/FreeML) :  A List of Data Science/Machine Learning Resources (Mostly Free) \n\n[Paper-List](https://github.com/ConanCui/Paper-List) :  A reading paper list which is mainted daily \n\n[awesome-jupyter](https://github.com/markusschanta/awesome-jupyter) :  A curated list of awesome Jupyter projects, libraries and resources \n\n[the-incredible-pytorch](https://github.com/ritchieng/the-incredible-pytorch) :  The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. \n\n[awesome-quant](https://github.com/wilsonfreitas/awesome-quant) :  A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance) \n\n[awesome-quant](https://github.com/thuquant/awesome-quant) :  quant related resources index in China \n\n[awesome-community-detection](https://github.com/benedekrozemberczki/awesome-community-detection) :  A curated list of community detection research papers with implementations. \n\n[awesome-robotics-libraries](https://github.com/jslee02/awesome-robotics-libraries) :  😎 A curated list of robotics libraries and software \n\n[awesome_time_series_in_python](https://github.com/MaxBenChrist/awesome_time_series_in_python) :  This curated list contains python packages for time series analysis \n\n[awesome-rnn](https://github.com/kjw0612/awesome-rnn) :  Recurrent Neural Network - A curated list of resources dedicated to RNN \n\n[awesome-random-forest](https://github.com/kjw0612/awesome-random-forest) :  Random Forest - a curated list of resources regarding random forest \n\n[awesome-adversarial-machine-learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning) :  A curated list of awesome adversarial machine learning resources \n\n[awesome-awesome](https://github.com/emijrp/awesome-awesome) :  A curated list of awesome curated lists of many topics. \n\n[VR-Awesome](https://github.com/Vytek/VR-Awesome) :  VR Awesome List \n\n[machine-learning-surveys](https://github.com/metrofun/machine-learning-surveys) :  A curated list of Machine Learning Surveys, Tutorials and Books. \n\n[awesome-rl](https://github.com/aikorea/awesome-rl) :  Reinforcement learning resources curated \n\n[awesome-knowledge-distillation](https://github.com/dkozlov/awesome-knowledge-distillation) :  Awesome Knowledge Distillation \n\n[Awesome-Incremental-Learning](https://github.com/xialeiliu/Awesome-Incremental-Learning) :  Awesome Incremental Learning \n\n[awesome-graph-classification](https://github.com/benedekrozemberczki/awesome-graph-classification) :  A collection of important graph embedding, classification and representation learning papers with implementations. \n\n[Awesome-Transformer-Attention](https://github.com/cmhungsteve/Awesome-Transformer-Attention) ： An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites \n\n[awesome-public-datasets](https://github.com/awesomedata/awesome-public-datasets) :  A topic-centric list of HQ open datasets. \n\n[awesome-ai-in-finance](https://github.com/georgezouq/awesome-ai-in-finance) :  🔬 A curated list of awesome machine learning strategies \u0026 tools in financial market. \n\n[Awesome-explainable-AI](https://github.com/wangyongjie-ntu/Awesome-explainable-AI) :  A collection of research materials on explainable AI/ML \n\n[Awesome-Federated-Learning](https://github.com/chaoyanghe/Awesome-Federated-Learning) :  FedML - The Research and Production Integrated Federated Learning Library\n\n[Awesome-AI-Security](https://github.com/DeepSpaceHarbor/Awesome-AI-Security) :  A curated list of AI security resources inspired by [awesome-adversarial-machine-learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning) \u0026 [awesome-ml-for-cybersecurity](https://github.com/jivoi/awesome-ml-for-cybersecurity). \n\n[awesome-deep-rl](https://github.com/tigerneil/awesome-deep-rl) :  For deep RL and the future of AI. \n\n[awesome-fashion-ai](https://github.com/ayushidalmia/awesome-fashion-ai) :  A repository to curate and summarise research papers related to fashion and e-commerce \n\n[awesome-blockchain-ai](https://github.com/steven2358/awesome-blockchain-ai) :  A curated list of Blockchain projects for Artificial Intelligence and Machine Learning \n\n[awesome-starcraftAI](https://github.com/SKTBrain/awesome-starcraftAI) :  A curated list of resources dedicated to StarCraft AI. \n\n[awesome-game-ai](https://github.com/datamllab/awesome-game-ai) :  Awesome Game AI materials of Multi-Agent Reinforcement Learning \n\n[awesome-feature-engineering](https://github.com/aikho/awesome-feature-engineering) :  A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning \n\n[awesome-ai-usecases](https://github.com/JosPolfliet/awesome-ai-usecases) :  A list of awesome and proven Artificial Intelligence use cases and applications \n\n[lite.ai.toolkit](https://github.com/DefTruth/lite.ai.toolkit) :  A lite C++ toolkit of awesome AI models with ONNXRuntime, NCNN, MNN and TNN. YOLOX, YOLOP, YOLOv6, YOLOR, MODNet, YOLOX, YOLOv7, YOLOv5. MNN, NCNN, TNN, ONNXRuntime. \n\n[awesome-ai](https://github.com/hades217/awesome-ai) :  A curated list of artificial intelligence resources (Courses, Tools, App, Open Source Project) \n\n[500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code](https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code) :  500 AI Machine learning Deep learning Computer vision NLP Projects with code \n\n[knowledge-distillation-papers](https://github.com/lhyfst/knowledge-distillation-papers) :  knowledge distillation papers \n\n[Awesome_Continual-Lifelong-Incremental_learning](https://github.com/chengsilin/Awesome_Continual-Lifelong-Incremental_learning) : Awesome Continual-Lifelong-Incremental learning\n\n[Awesome-Few-Shot-Class-Incremental-Learning](https://github.com/zhoudw-zdw/Awesome-Few-Shot-Class-Incremental-Learning) : Awesome Few-Shot Class Incremental Learning\n\n[awesome-gcn](https://github.com/Jiakui/awesome-gcn) :  resources for graph convolutional networks \n\n[Awesome-Deep-Graph-Clustering](https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering) :  Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets). \n\n[awesome-denovo-papers](https://github.com/asarigun/awesome-denovo-papers) :  Awesome De novo drugs design papers \n\n[awesome-lidar](https://github.com/szenergy/awesome-lidar) :  Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators. \n\n[awesome-self-supervised-gnn](https://github.com/ChandlerBang/awesome-self-supervised-gnn) :  Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN). \n\n[ai-collection](https://github.com/ai-collection/ai-collection) : The Generative AI Landscape - A Collection of Awesome Generative AI Applications\n\n[papers-we-love](https://github.com/papers-we-love/papers-we-love) :  Papers from the computer science community to read and discuss. \n\n[Awesome-Autonomous-Driving](https://github.com/autodriving-heart/Awesome-Autonomous-Driving) :  awesome-autonomous-driving \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbigcash%2Fawesome-ai-list-guide","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbigcash%2Fawesome-ai-list-guide","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbigcash%2Fawesome-ai-list-guide/lists"}