{"id":13559744,"url":"https://github.com/NirantK/awesome-project-ideas","last_synced_at":"2025-04-03T15:30:54.794Z","repository":{"id":38428163,"uuid":"100784668","full_name":"NirantK/awesome-project-ideas","owner":"NirantK","description":"Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas","archived":false,"fork":false,"pushed_at":"2023-03-13T16:28:22.000Z","size":113,"stargazers_count":7571,"open_issues_count":2,"forks_count":1218,"subscribers_count":289,"default_branch":"master","last_synced_at":"2024-05-19T17:14:48.852Z","etag":null,"topics":["awesome","awesome-list","classification","dataset","deep-learning","forecasting","image-classification","machine-learning","multi-label-classification","series-forecasting"],"latest_commit_sha":null,"homepage":"http://www.nirantk.com/awesome-project-ideas/","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/NirantK.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}},"created_at":"2017-08-19T09:37:58.000Z","updated_at":"2024-05-19T17:14:48.853Z","dependencies_parsed_at":"2023-09-22T07:02:41.185Z","dependency_job_id":null,"html_url":"https://github.com/NirantK/awesome-project-ideas","commit_stats":{"total_commits":64,"total_committers":7,"mean_commits":9.142857142857142,"dds":0.09375,"last_synced_commit":"31f6fb0dba79430f220ee9b77e773ed496193be6"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NirantK%2Fawesome-project-ideas","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NirantK%2Fawesome-project-ideas/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NirantK%2Fawesome-project-ideas/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NirantK%2Fawesome-project-ideas/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/NirantK","download_url":"https://codeload.github.com/NirantK/awesome-project-ideas/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":222818765,"owners_count":17042230,"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":["awesome","awesome-list","classification","dataset","deep-learning","forecasting","image-classification","machine-learning","multi-label-classification","series-forecasting"],"created_at":"2024-08-01T13:00:32.434Z","updated_at":"2024-11-04T11:30:41.513Z","avatar_url":"https://github.com/NirantK.png","language":null,"funding_links":[],"categories":["HarmonyOS","Others","😍 Other community repos about ideas!","Tutorials","▶️ Content","A01_机器学习教程","100 + 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗟𝗶𝘀𝘁 𝘄𝗶𝘁𝗵 𝗰𝗼𝗱𝗲","Table of Contents","Other Lists","Deep-Learning","Writing \u0026 Editing","📦 Legacy \u0026 Inactive Projects","📚 Project Purpose"],"sub_categories":["Windows Manager","Projects","TeX Lists","Machine Learning (Interview-Level"],"readme":"\u003c!-- markdownlint-disable MD033 --\u003e\n\n# Awesome Deep Learning Project Ideas\n\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\nA curated list of practical deep learning and machine learning project ideas\n\n- 30+ ideas\n- Relevant to both the academia and industry\n- Ranges from beginner friendly to research projects\n\n---\n\n## Contents\n\n- [Hackathon Ideas](#hackathon-ideas) - Project ideas unlocked by use of Large Language Models, specially text to text -- note that a lot of the text to text ideas can also be buit a lot better with LLMs now!\n\n- [Text](#text) - With some topics about Natural language processing\n\n- [Forecasting](#forecasting) - Most of the topics in this section is about Time Series and similar forecasting challenges\n\n- [Recommendation Systems](#recommendation-systems)\n\n- [Vision](#vision) - With topics about image and video processing\n\n- [Music and Audio](#music) - These topics are about combining ideas from language and audio to understand music\n\n- [Conclusion](#conclusion)\n\n---\n\n## Hackathon Ideas\n\n- **Developer Ideas**\n  - Text to cmd for terminal: Take user intent in terminal e.g. \n    ```bash\n    $ask \"how to list all files with details\"\n    \u003e Execute \"ls -l\"? [y/N] y \n    $ls -l\n    ```\n  - Build and edit YAMLs using natural language e.g. Kubernetes and other form of config files\n    - [Kor](eyurtsev.github.io/kor/) for ideas on how this is done for JSON\n    - Can be use-case specific. Build pipelines? Kube?\n    \n  - Mobile Android/iOS SDK for Stable Diffusion inference\n    -  Apple has released a [CoreML Stable Diffusion Inference](https://github.com/apple/ml-stable-diffusion)\n\n- **Voice powered Experiences**\n  - Audio Conversation with chatGPT, can combine with fast Text-to-Speech e.g. [Eleven Labs](https://elevenlabs.io) to have a two-way conversation\n  - Telegram/WhatsApp bot to get audio and save as text with metadata into mem.ai or Roam Research or Obsidian\n\n- Edit image by giving instructions of what you want to do: [SeeChatGPT](https://github.com/Nischaydnk/SeeChatGPT) and [playgroundai.com](playgroundai.com) as examples\n  - The underlying mechanism which you can use is called [InstructPix2Pix](huggingface.co/spaces/timbrooks/instruct-pix2pix)\n  \n- Semantic search over any media\n  - Can build using CLIP or [BLIP-2 embeddings](huggingface.co/docs/transformers/main/model_doc/blip-2) for images and [CLAP](https://github.com/LAION-AI/CLAP/tree/clap#quick-start) for all audio including music and speech\n\n- Text to Music Generation\n  - See [MusicLM](https://google-research.github.io/seanet/musiclm/examples/) for reference\n  \n- **Knowledge Base QA** aka Answer Engines\n\n  - Take any plaintext dataset e.g. State of the Union address and build on top of that \n  ![image](https://user-images.githubusercontent.com/3250749/223094577-8126570b-f7a4-48ad-9f77-ff86a8b21161.png)\n  - Can use this over Video Subtitles to search and QA over videos as well, by mapping back to source\n\n- **Guided Summarisation/Rewriting**\n  \n  - Take specific questions which the user might have about a large text dataset e.g. a novel or book and include that in your summary of the piece\n  - Pay attention to specific entities and retell the events which happen in a story with attention to that character\n  \n- **ControlNet + Stable Diffusion for Aethetic Control**\n  - Build tooling using [diffusers](https://github.com/huggingface/diffusers/) which takes in a set of photos, finetunes a model (LoRA) on a person, detects face and moves it to a new aesthetic e.g. futuristic neon punk, grunge rock, Studio Ghibli. Can also add InstructPix2Pix to give user more control. \n  \n- **Text to Code/SQL**\n\n  - Use code understanding to convert use query to SQL or another executable programming language, including Domain Specific Languages\n  - Here is an example of the same: [qabot](github.com/hardbyte/qabot)\n  \n## Text\n\n- **Autonomous Tagging of StackOverflow Questions**\n    - Make a multi-label classification system that automatically assigns tags for questions posted on a forum such as StackOverflow or Quora.\n    - Dataset: [StackLite](https://www.kaggle.com/stackoverflow/stacklite) or [10% sample](https://www.kaggle.com/stackoverflow/stacksample)\n\n- **Keyword/Concept identification**\n  \n  - Identify keywords from millions of questions\n  - Dataset: [StackOverflow question samples by Facebook](https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/data)\n\n- **Topic identification**\n  - Multi-label classification of printed media articles to topics\n  - Dataset: [Greek Media monitoring multi-label classification](https://www.kaggle.com/c/wise-2014/data)\n\n### Natural Language Understanding\n\n- **Sentence to Sentence semantic similarity**\n  - Can you identify question pairs that have the same intent or meaning?\n  - Dataset: [Quora question pairs](https://www.kaggle.com/c/quora-question-pairs/data) with similar questions marked\n\n- **Fight online abuse**\n  - Can you confidently and accurately tell whether a particular comment is abusive?\n  - Dataset: [Toxic comments on Kaggle](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)\n\n- **Open Domain question answering**\n  - Can you build a bot which answers questions according to the student's age or her curriculum?\n  - [Facebook's FAIR](https://github.com/facebookresearch/DrQA) is built in a similar way for Wikipedia.\n  - Dataset: [NCERT books](https://ncert.nic.in/textbook.php) for K-12/school students in India, [NarrativeQA by Google DeepMind](https://github.com/deepmind/narrativeqa) and [SQuAD by Stanford](https://rajpurkar.github.io/SQuAD-explorer/)\n\n- **Automatic text summarization**\n  - Can you create a summary with the major points of the original document?\n  - Abstractive (write your own summary) and Extractive (select pieces of text from original) are two popular approaches\n  - Dataset: [CNN and DailyMail News Pieces](http://cs.nyu.edu/~kcho/DMQA/) by Google DeepMind\n\n- **Copy-cat Bot**\n  - Generate plausible new text which looks like some other text\n  - Obama Speeches? For instance, you can create a bot which writes some [new speeches in Obama's style](https://medium.com/@samim/obama-rnn-machine-generated-political-speeches-c8abd18a2ea0)\n  - Trump Bot? Or a Twitter bot which mimics [@realDonaldTrump](http://www.twitter.com/@realdonaldtrump)\n  - Narendra Modi bot saying \"*doston*\"? Start by scrapping off his *Hindi* speeches from his [personal website](http://www.narendramodi.in)\n  - Example Dataset: [English Transcript of Modi speeches](https://github.com/mgupta1410/pm_modi_speeches_repo)\n\nCheck [mlm/blog](http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/) for some hints.\n\n- **Sentiment Analysis**\n  - Do Twitter Sentiment Analysis on tweets sorted by geography and timestamp.\n  - Dataset: [Tweets sentiment tagged by humans](https://inclass.kaggle.com/c/si650winter11/data)\n\n## Forecasting\n\n- **Univariate Time Series Forecasting**\n  - How much will it rain this year?\n  - Dataset: [45 years of rainfall data](http://research.jisao.washington.edu/data_sets/widmann/)\n\n- **Multi-variate Time Series Forecasting**\n  - How polluted will your town's air be? Pollution Level Forecasting\n  - Dataset: [Air Quality dataset](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data)\n\n- **Demand/load forecasting**\n  - Find a short term forecast on electricity consumption of a single home\n  - Dataset: [Electricity consumption of a household](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption)\n\n- **Predict Blood Donation**\n  - We're interested in predicting if a blood donor will donate within a given time window.\n  - More on the problem statement at [Driven Data](https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/page/7/).\n  - Dataset: [UCI ML Datasets Repo](https://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center)\n\n## Recommendation systems\n\n- **Movie Recommender**\n  - Can you predict the rating a user will give on a movie?\n  - Do this using the movies that user has rated in the past, as well as the ratings similar users have given similar movies.\n  - Dataset: [Netflix Prize](http://www.netflixprize.com/) and [MovieLens Datasets](https://grouplens.org/datasets/movielens/)\n\n- **Search + Recommendation System**\n  - Predict which Xbox game a visitor will be most interested in based on their search query\n  - Dataset: [BestBuy](https://www.kaggle.com/c/acm-sf-chapter-hackathon-small/data)\n\n- **Can you predict Influencers in the Social Network?**\n  - How can you predict social influencers?\n  - Dataset: [PeerIndex](https://www.kaggle.com/c/predict-who-is-more-influential-in-a-social-network/data)\n\n## Vision\n\n- **Image classification**\n  - Object recognition or image classification task is how Deep Learning shot up to it's present-day resurgence\n  - Datasets:\n    - [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html)\n    - [ImageNet](http://www.image-net.org/)\n    - [MS COCO](http://mscoco.org/) is the modern replacement to the ImageNet challenge\n    - [MNIST Handwritten Digit Classification Challenge](http://yann.lecun.com/exdb/mnist/)  is the classic entry point\n    - [Character recognition (digits)](http://ai.stanford.edu/~btaskar/ocr/) is the good old Optical Character Recognition problem\n    - Bird Species Identification from an Image using the [Caltech-UCSD Birds dataset](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html) dataset\n  - Diagnosing and Segmenting Brain Tumors and Phenotypes using MRI Scans\n    - Dataset: MICCAI Machine Learning Challenge aka [MLC 2014](https://www.nmr.mgh.harvard.edu/lab/laboratory-computational-imaging-biomarkers/miccai-2014-machine-learning-challenge)\n  - Identify endangered right whales in aerial photographs\n    - Dataset: [MOAA Right Whale](https://www.kaggle.com/c/noaa-right-whale-recognition)\n  - Can computer vision spot distracted drivers?\n    - Dataset: [State Farm Distracted Driver Detection](https://www.kaggle.com/c/state-farm-distracted-driver-detection/data) on Kaggle\n\n- **Bone X-Ray competition**\n  - Can you identify if a hand is broken from a X-ray radiographs automatically with better than human performance?\n  - Stanford's Bone XRay Deep Learning Competition with [MURA Dataset](https://stanfordmlgroup.github.io/competitions/mura/)\n\n- **Image Captioning**\n  - Can you caption/explain the photo a way human would?\n  - Dataset: [MS COCO](http://mscoco.org/dataset/#captions-challenge2015)\n\n- **Image Segmentation/Object Detection**\n  - Can you extract an object of interest from an image?\n  - Dataset: [MS COCO](http://mscoco.org/dataset/#detections-challenge2017), [Carvana Image Masking Challenge](https://www.kaggle.com/c/carvana-image-masking-challenge/data) on Kaggle\n\n- **Large-Scale Video Understanding**\n  - Can you produce the best video tag predictions?\n  - Dataset: [YouTube 8M](https://research.google.com/youtube8m/index.html)\n\n- **Video Summarization**\n  - Can you select the semantically relevant/important parts from the video?\n  - Example: [Fast-Forward Video Based on Semantic Extraction](https://arxiv.org/abs/1708.04160)\n  - Dataset: Unaware of any standard dataset or agreed upon metrics? I think [YouTube 8M](https://research.google.com/youtube8m/index.html) might be good starting point.\n\n- **Style Transfer**\n  - Can you recompose images in the style of other images?\n  - Dataset: [fzliu on GitHub](https://github.com/fzliu/style-transfer/tree/master/images) shared target and source images with results\n\n- **Chest XRay**\n  - Can you detect if someone is sick from their chest XRay? Or guess their radiology report?\n  - Dataset: [MIMIC-CXR at Physionet](https://physionet.org/content/mimic-cxr/2.0.0/)\n\n- **Clinical Diagnostics: Image Identification, classification \u0026 segmentation**\n  - Can you help build an open source software for lung cancer detection to help radiologists?\n  - Link: [Concept to clinic](https://concepttoclinic.drivendata.org/) challenge on DrivenData\n\n- **Satellite Imagery Processing for Socioeconomic Analysis**\n  - Can you estimate the standard of living or energy consumption of a place from night time satellite imagery?\n  - Reference for Project details: [Stanford Poverty Estimation Project](http://sustain.stanford.edu/predicting-poverty/)\n\n- **Satellite Imagery Processing for Automated Tagging**\n  - Can you automatically tag satellite images with human features such as buildings, roads, waterways and so on?\n  - Help free the manual effort in tagging satellite imagery: [Kaggle Dataset by DSTL, UK](https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection)\n\n## Music\n\n- **Music/Audio Recommendation Systems**\n  - Can you tell if two songs are similar using their sound or lyrics?\n  - Dataset: [Million Songs Dataset](https://labrosa.ee.columbia.edu/millionsong/) and it's 1% sample.\n  - Example: [Anusha et al](https://cs224d.stanford.edu/reports/BalakrishnanDixit.pdf)\n\n- **Music Genre recognition using neural networks**\n  - Can you identify the musical genre using their spectrograms or other sound information?\n  - Datasets: [FMA](https://github.com/mdeff/fma) or [GTZAN on Keras](https://github.com/Hguimaraes/gtzan.keras)\n  - Get started with [Librosa](https://librosa.github.io/librosa/index.html) for feature extraction\n\n\n\n---\n\n### FAQ\n\n- **Can I use the ideas here for my thesis?**\n  Yes, totally! I'd love to know how it went.\n\n- **Do you have any advice before I start my project?**\n  [Advice for Short Term Machine Learning Projects](https://rockt.github.io/2018/08/29/msc-advice) by Tim R. is a pretty good starting point!\n\n- **How can I add my ideas here?**\n  Just send a pull request and we'll discuss?\n\n- **Hey, something is wrong here!**\n  Yikes, I am sorry. Please tell me by raising a [GitHub issue](https://github.com/NirantK/awesome-project-ideas/issues).\n\n  I'll fix it as soon as possible.\n\n### Acknowledgements\n\nProblems are motivated by the ones shared at:\n\n- [CMU Machine Learning](http://www.cs.cmu.edu/~./10701/projects.html)\n- [Stanford CS229 Machine Learning Projects](http://cs229.stanford.edu/)\n- [swyx](https://github.com/sw-yx/ai-notes/blob/main/Resources/AI-hackathon-stack.md)\n\n### Credit\n\nBuilt with lots of keyboard smashing and copy-pasta love by NirantK. Find me on [Twitter](http://www.twitter.com/@nirantk)!\n\n### License\n\nThis repository is licensed under the MIT License. Please see the [LICENSE file](./LICENSE) for more details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FNirantK%2Fawesome-project-ideas","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FNirantK%2Fawesome-project-ideas","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FNirantK%2Fawesome-project-ideas/lists"}