Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Machine Learning (Beginners Hub), information(courses, books, cheat sheets, live sessions) related to machine learning, data science and python is available
| Date | Stars |
|---|---|
| 2026-07-31 | 380 |
| 2026-08-01 | 380 |
| 2026-08-06 | 380 |
Today
— stars today
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Momentum
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growth rate 0.00%/day
# One Stop for machine learning <!-- 1. [Papers](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L35) 2. [Languages](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L69) 3. [Platforms](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L79) 4. [Courses](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L94) 5. [Live Sessions](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L157) 6. [Books](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L164) 7. [Cheat sheets](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L176) 8. [Interview Questions](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L200) 9. [DataSets](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L209) 10. [Blogs and Community](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L221) 11. [Online Competitions](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L230) 12. [Other Sources](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L239) 13. [About CNNs](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L282) 14. [About GANs](https://github.com/AdicherlaVenkataSai/ml-workspace/blame/master/README.md#L318) --> **Sections:** 1. Papers 2. Languages 3. Platforms 4. Courses 5. Live Sessions 6. Books 7. Cheat sheets 8. Interview Questions 9. DataSets 10. Blogs and Community 11. Online Competitions 12. Other Sources 13. About CNNs 14. About GANs **Steps in approaching a Machine learning problem:** Below are the steps that I follow while approaching a ML problem. - Defining and understanding the problem statement - Gathering the Data - Initial Exploration of Data - In-depth EDA - Building the model - Analyzing the results with different models and shortlisting the ones which gives good performance measures - Fine-tuning the selected model - Document the code - Deployment - Monitoring the deployed model performance in real time. ## Papers | 2020 <!--Natural Language Processing --> - [Towards a Human-like Open-Domain Chatbot](https://arxiv.org/abs/2001.09977) - [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://arxiv.org/abs/2003.10555) - [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) - [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) - [You Impress Me: Dialogue Generation via Mutual Persona Perception](https://arxiv.org/abs/2004.05388) - [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) - [ToD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogues](https://arxiv.org/abs/2004.06871) - [SOLOIST: Few-shot Task-Oriented Dialog with A Single Pre-trained Auto-regressive Model](https://arxiv.org/abs/2005.05298) - [A Simple Language Model for Task-Oriented Dialogue](https://arxiv.org/abs/2005.00796) - [FastBERT: a Self-distilling BERT with Adaptive Inference Time](https://arxiv.org/abs/2004.02178) - [PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination](https://arxiv.org/abs/2001.08950) - [Data Augmentation using Pre-trained Transformer Models](https://arxiv.org/abs/2003.02245) - [FLAT: Chinese NER Using Flat-Lattice Transformer](https://arxiv.org/abs/2004.11795) <!--Object Detection --> - [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) | [code](https://github.com/facebookresearch/detr) - [Objects as Points](https://arxiv.org/abs/1904.07850) | [code](https://github.com/xingyizhou/CenterNet) - [Acquisition of Localization Confidence for Accurate Object Detection](https://arxiv.org/abs/1807.11590) | [code](https://github.com/v
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0128849ff8acdcc6, llm:Repository topics: cheat-sheets, convolutional-networks, data-science, deep-learning, deep-neural-networks, gans, harvard-edx, interview-questions, machine-learning, python; README describes curated list of papers, courses, books, cheat sheets, datasets, interview questions and learning resources for ML and data science.
matched fp:0128849ff8acdcc6, llm:Repository topics: cheat-sheets, convolutional-networks, data-science, deep-learning, deep-neural-networks, gans, harvard-edx, interview-questions, machine-learning, python; README describes curated list of papers, courses, books, cheat sheets, datasets, interview questions and learning resources for ML and data science.
matched fp:0128849ff8acdcc6, llm:Repository topics: cheat-sheets, convolutional-networks, data-science, deep-learning, deep-neural-networks, gans, harvard-edx, interview-questions, machine-learning, python; README describes curated list of papers, courses, books, cheat sheets, datasets, interview questions and learning resources for ML and data science.