upgini
LeanDojoChatGPT
| upgini | LeanDojoChatGPT | |
|---|---|---|
| 16 | 2 | |
| 290 | 99 | |
| 2.4% | - | |
| 9.1 | 5.3 | |
| 4 days ago | about 1 month ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
upgini
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The fastest way to improve quality of ML model on tabular data
web: https://upgini.com
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How to enrich ML models with open data for free: an in-depth review of 5 python libraries
The code is on GitHub.
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How I complete my email addresses lists with demographic insights with Python
Now let’s launch the search for gender-correlated features. For this operation, I will need a free API token that I get after sign up on upgini.com. The API token is free and using to search features by personal keys such as email, phone number, and IP address.
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[OC] Divorced relationship status share of users at Facebook
Source: Upgini database Made with MapChart
- GitHub - searching open and public data through autoML. Please give a Star on GitHub
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[P] Upgini 1.0 is released (a Python library for data search through autoML )
Full release notes: https://github.com/upgini/upgini
- Need your help with GitHub
- Upgini.com: Public data search engine for ML that helps DS to reach best accuracy with external features
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Python library for free data search & enrichment
GitHub
LeanDojoChatGPT
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'A-Team' of Math Proves a Critical Link Between Addition and Sets
Check out this paper:
https://leandojo.org/
People have already trained models to assist suggestion tactics. They then linked it up to ChatGPT to interactively solve proofs.
In this scenario, ChatGPT asks the model for tactic suggestions, applies it to the proof and uses the feedback from Lean to then proceed with the next step.
FYI, The programmatic interface to Lean was written by an OpenAI employee who was on the Lean team a few years ago.
Also, check out Lean’s roadmap. They aspire to position Lean to becoming a target for LLMs because it has been designed for verification from the ground up.
As math and compsci nerds contribute to mathlib, all of those proofs are also building up a huge corpus that will likely be leveraged for both verification and optimization.
If AI can make verification a lot easier, then we’re likely going to see verification change programming similarly to the way it changed electronics.
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Formalizing 100 Theorems
Good questions!
Nowadays, there is indeed a movement towards interoperability between the various proof assistants, one of these bridge-building projects is called Dedukti: https://deducteam.github.io/ It's a challenging project because the different proof assistants which are currently in use differ in their foundational perspectives and their idioms. The question how to best formalize mathematics is still an open research problem, just as the question how to best develop programs, but we already have quite a good understanding of many important issues in this area.
Also, by now there are attempts to use AI for discovering proofs, see for instance https://leandojo.org/ or https://github.com/lean-dojo/LeanDojoChatGPT.
What are some alternatives?
NitroFE - NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.
marqo - Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
featuretools - An open source python library for automated feature engineering
FlexGen - Running large language models on a single GPU for throughput-oriented scenarios.
powershap - A power-full Shapley feature selection method.
set.mm - Metamath source file for logic and set theory
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
linc - 🔗 LINC: Logical Inference via Neurosymbolic Computation [EMNLP2023]
fibs-reporter - Automatically generate a pdf report containing feature importance, baseline modelling, spurious correlation detection, and more, from a single command line input for any given ML CSV file
FlexGen - Running large language models like OPT-175B/GPT-3 on a single GPU. Focusing on high-throughput generation. [Moved to: https://github.com/FMInference/FlexGen]
atariemailarchive-data - A structured dataset of emails sent at Atari from 1983 to 1992.
ChatGPT-API-Python - Building a Chatbot in Python using OpenAI's Official ChatGPT API