Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/NirantK/awesome-project-ideas

Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas
https://github.com/NirantK/awesome-project-ideas

List: awesome-project-ideas

awesome awesome-list classification dataset deep-learning forecasting image-classification machine-learning multi-label-classification series-forecasting

Last synced: 3 months ago
JSON representation

Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

Awesome Lists containing this project

README 

        



# Awesome Deep Learning Project Ideas



[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)



A curated list of practical deep learning and machine learning project ideas



- 30+ ideas

- Relevant to both the academia and industry

- Ranges from beginner friendly to research projects



---



## Contents



- [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!



- [Text](#text) - With some topics about Natural language processing



- [Forecasting](#forecasting) - Most of the topics in this section is about Time Series and similar forecasting challenges



- [Recommendation Systems](#recommendation-systems)



- [Vision](#vision) - With topics about image and video processing



- [Music and Audio](#music) - These topics are about combining ideas from language and audio to understand music



- [Conclusion](#conclusion)



---



## Hackathon Ideas



- **Developer Ideas**

  - Text to cmd for terminal: Take user intent in terminal e.g. 

    ```bash

    $ask "how to list all files with details"

    > Execute "ls -l"? [y/N] y 

    $ls -l

    ```

  - Build and edit YAMLs using natural language e.g. Kubernetes and other form of config files

    - [Kor](eyurtsev.github.io/kor/) for ideas on how this is done for JSON

    - Can be use-case specific. Build pipelines? Kube?

    

  - Mobile Android/iOS SDK for Stable Diffusion inference

    -  Apple has released a [CoreML Stable Diffusion Inference](https://github.com/apple/ml-stable-diffusion)



- **Voice powered Experiences**

  - Audio Conversation with chatGPT, can combine with fast Text-to-Speech e.g. [Eleven Labs](https://elevenlabs.io) to have a two-way conversation

  - Telegram/WhatsApp bot to get audio and save as text with metadata into mem.ai or Roam Research or Obsidian



- Edit image by giving instructions of what you want to do: [SeeChatGPT](https://github.com/Nischaydnk/SeeChatGPT) and [playgroundai.com](playgroundai.com) as examples

  - The underlying mechanism which you can use is called [InstructPix2Pix](huggingface.co/spaces/timbrooks/instruct-pix2pix)

  

- Semantic search over any media

  - 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



- Text to Music Generation

  - See [MusicLM](https://google-research.github.io/seanet/musiclm/examples/) for reference

  

- **Knowledge Base QA** aka Answer Engines



  - Take any plaintext dataset e.g. State of the Union address and build on top of that 

  ![image](https://user-images.githubusercontent.com/3250749/223094577-8126570b-f7a4-48ad-9f77-ff86a8b21161.png)

  - Can use this over Video Subtitles to search and QA over videos as well, by mapping back to source



- **Guided Summarisation/Rewriting**

  

  - 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

  - Pay attention to specific entities and retell the events which happen in a story with attention to that character

  

- **ControlNet + Stable Diffusion for Aethetic Control**

  - 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. 

  

- **Text to Code/SQL**



  - Use code understanding to convert use query to SQL or another executable programming language, including Domain Specific Languages

  - Here is an example of the same: [qabot](github.com/hardbyte/qabot)

  

## Text



- **Autonomous Tagging of StackOverflow Questions**

    - Make a multi-label classification system that automatically assigns tags for questions posted on a forum such as StackOverflow or Quora.

    - Dataset: [StackLite](https://www.kaggle.com/stackoverflow/stacklite) or [10% sample](https://www.kaggle.com/stackoverflow/stacksample)



- **Keyword/Concept identification**

  

  - Identify keywords from millions of questions

  - Dataset: [StackOverflow question samples by Facebook](https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/data)



- **Topic identification**

  - Multi-label classification of printed media articles to topics

  - Dataset: [Greek Media monitoring multi-label classification](https://www.kaggle.com/c/wise-2014/data)



### Natural Language Understanding



- **Sentence to Sentence semantic similarity**

  - Can you identify question pairs that have the same intent or meaning?

  - Dataset: [Quora question pairs](https://www.kaggle.com/c/quora-question-pairs/data) with similar questions marked



- **Fight online abuse**

  - Can you confidently and accurately tell whether a particular comment is abusive?

  - Dataset: [Toxic comments on Kaggle](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)



- **Open Domain question answering**

  - Can you build a bot which answers questions according to the student's age or her curriculum?

  - [Facebook's FAIR](https://github.com/facebookresearch/DrQA) is built in a similar way for Wikipedia.

  - 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/)



- **Automatic text summarization**

  - Can you create a summary with the major points of the original document?

  - Abstractive (write your own summary) and Extractive (select pieces of text from original) are two popular approaches

  - Dataset: [CNN and DailyMail News Pieces](http://cs.nyu.edu/~kcho/DMQA/) by Google DeepMind



- **Copy-cat Bot**

  - Generate plausible new text which looks like some other text

  - 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)

  - Trump Bot? Or a Twitter bot which mimics [@realDonaldTrump](http://www.twitter.com/@realdonaldtrump)

  - Narendra Modi bot saying "*doston*"? Start by scrapping off his *Hindi* speeches from his [personal website](http://www.narendramodi.in)

  - Example Dataset: [English Transcript of Modi speeches](https://github.com/mgupta1410/pm_modi_speeches_repo)



Check [mlm/blog](http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/) for some hints.



- **Sentiment Analysis**

  - Do Twitter Sentiment Analysis on tweets sorted by geography and timestamp.

  - Dataset: [Tweets sentiment tagged by humans](https://inclass.kaggle.com/c/si650winter11/data)



## Forecasting



- **Univariate Time Series Forecasting**

  - How much will it rain this year?

  - Dataset: [45 years of rainfall data](http://research.jisao.washington.edu/data_sets/widmann/)



- **Multi-variate Time Series Forecasting**

  - How polluted will your town's air be? Pollution Level Forecasting

  - Dataset: [Air Quality dataset](https://archive.ics.uci.edu/ml/datasets/Beijing+PM2.5+Data)



- **Demand/load forecasting**

  - Find a short term forecast on electricity consumption of a single home

  - Dataset: [Electricity consumption of a household](https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption)



- **Predict Blood Donation**

  - We're interested in predicting if a blood donor will donate within a given time window.

  - More on the problem statement at [Driven Data](https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/page/7/).

  - Dataset: [UCI ML Datasets Repo](https://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center)



## Recommendation systems



- **Movie Recommender**

  - Can you predict the rating a user will give on a movie?

  - Do this using the movies that user has rated in the past, as well as the ratings similar users have given similar movies.

  - Dataset: [Netflix Prize](http://www.netflixprize.com/) and [MovieLens Datasets](https://grouplens.org/datasets/movielens/)



- **Search + Recommendation System**

  - Predict which Xbox game a visitor will be most interested in based on their search query

  - Dataset: [BestBuy](https://www.kaggle.com/c/acm-sf-chapter-hackathon-small/data)



- **Can you predict Influencers in the Social Network?**

  - How can you predict social influencers?

  - Dataset: [PeerIndex](https://www.kaggle.com/c/predict-who-is-more-influential-in-a-social-network/data)



## Vision



- **Image classification**

  - Object recognition or image classification task is how Deep Learning shot up to it's present-day resurgence

  - Datasets:

    - [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html)

    - [ImageNet](http://www.image-net.org/)

    - [MS COCO](http://mscoco.org/) is the modern replacement to the ImageNet challenge

    - [MNIST Handwritten Digit Classification Challenge](http://yann.lecun.com/exdb/mnist/)  is the classic entry point

    - [Character recognition (digits)](http://ai.stanford.edu/~btaskar/ocr/) is the good old Optical Character Recognition problem

    - Bird Species Identification from an Image using the [Caltech-UCSD Birds dataset](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html) dataset

  - Diagnosing and Segmenting Brain Tumors and Phenotypes using MRI Scans

    - Dataset: MICCAI Machine Learning Challenge aka [MLC 2014](https://www.nmr.mgh.harvard.edu/lab/laboratory-computational-imaging-biomarkers/miccai-2014-machine-learning-challenge)

  - Identify endangered right whales in aerial photographs

    - Dataset: [MOAA Right Whale](https://www.kaggle.com/c/noaa-right-whale-recognition)

  - Can computer vision spot distracted drivers?

    - Dataset: [State Farm Distracted Driver Detection](https://www.kaggle.com/c/state-farm-distracted-driver-detection/data) on Kaggle



- **Bone X-Ray competition**

  - Can you identify if a hand is broken from a X-ray radiographs automatically with better than human performance?

  - Stanford's Bone XRay Deep Learning Competition with [MURA Dataset](https://stanfordmlgroup.github.io/competitions/mura/)



- **Image Captioning**

  - Can you caption/explain the photo a way human would?

  - Dataset: [MS COCO](http://mscoco.org/dataset/#captions-challenge2015)



- **Image Segmentation/Object Detection**

  - Can you extract an object of interest from an image?

  - 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



- **Large-Scale Video Understanding**

  - Can you produce the best video tag predictions?

  - Dataset: [YouTube 8M](https://research.google.com/youtube8m/index.html)



- **Video Summarization**

  - Can you select the semantically relevant/important parts from the video?

  - Example: [Fast-Forward Video Based on Semantic Extraction](https://arxiv.org/abs/1708.04160)

  - 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.



- **Style Transfer**

  - Can you recompose images in the style of other images?

  - Dataset: [fzliu on GitHub](https://github.com/fzliu/style-transfer/tree/master/images) shared target and source images with results



- **Chest XRay**

  - Can you detect if someone is sick from their chest XRay? Or guess their radiology report?

  - Dataset: [MIMIC-CXR at Physionet](https://physionet.org/content/mimic-cxr/2.0.0/)



- **Clinical Diagnostics: Image Identification, classification & segmentation**

  - Can you help build an open source software for lung cancer detection to help radiologists?

  - Link: [Concept to clinic](https://concepttoclinic.drivendata.org/) challenge on DrivenData



- **Satellite Imagery Processing for Socioeconomic Analysis**

  - Can you estimate the standard of living or energy consumption of a place from night time satellite imagery?

  - Reference for Project details: [Stanford Poverty Estimation Project](http://sustain.stanford.edu/predicting-poverty/)



- **Satellite Imagery Processing for Automated Tagging**

  - Can you automatically tag satellite images with human features such as buildings, roads, waterways and so on?

  - Help free the manual effort in tagging satellite imagery: [Kaggle Dataset by DSTL, UK](https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection)



## Music



- **Music/Audio Recommendation Systems**

  - Can you tell if two songs are similar using their sound or lyrics?

  - Dataset: [Million Songs Dataset](https://labrosa.ee.columbia.edu/millionsong/) and it's 1% sample.

  - Example: [Anusha et al](https://cs224d.stanford.edu/reports/BalakrishnanDixit.pdf)



- **Music Genre recognition using neural networks**

  - Can you identify the musical genre using their spectrograms or other sound information?

  - Datasets: [FMA](https://github.com/mdeff/fma) or [GTZAN on Keras](https://github.com/Hguimaraes/gtzan.keras)

  - Get started with [Librosa](https://librosa.github.io/librosa/index.html) for feature extraction



---



### FAQ



- **Can I use the ideas here for my thesis?**

  Yes, totally! I'd love to know how it went.



- **Do you have any advice before I start my project?**

  [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!



- **How can I add my ideas here?**

  Just send a pull request and we'll discuss?



- **Hey, something is wrong here!**

  Yikes, I am sorry. Please tell me by raising a [GitHub issue](https://github.com/NirantK/awesome-project-ideas/issues).



  I'll fix it as soon as possible.



### Acknowledgements



Problems are motivated by the ones shared at:



- [CMU Machine Learning](http://www.cs.cmu.edu/~./10701/projects.html)

- [Stanford CS229 Machine Learning Projects](http://cs229.stanford.edu/)

- [swyx](https://github.com/sw-yx/ai-notes/blob/main/Resources/AI-hackathon-stack.md)



### Credit



Built with lots of keyboard smashing and copy-pasta love by NirantK. Find me on [Twitter](http://www.twitter.com/@nirantk)!



### License



This repository is licensed under the MIT License. Please see the [LICENSE file](./LICENSE) for more details.