gretel-synthetics
ydata-synthetic
gretel-synthetics | ydata-synthetic | |
---|---|---|
4 | 60 | |
535 | 1,292 | |
3.2% | 2.8% | |
7.2 | 7.3 | |
5 days ago | 7 days ago | |
Python | Jupyter Notebook | |
GNU General Public License v3.0 or later | 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.
gretel-synthetics
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Ask HN: If we train an LLM with “data” instead of “language” tokens
Hey there! Co-founder of Gretel.ai here, and I think I can provide some insights on this topic.
Firstly, the concept you're hinting at is not purely traditional ML. In traditional machine learning, we often prioritize feature extraction and engineering specific to a given problem space before training.
What you're describing and what we've been working on at Gretel.ai, is leveraging the power of models like Large Language Models (LLMs) to understand and extrapolate from vast amounts of diverse data without the need for time-consuming feature engineering. Here's a link to our open-source library https://github.com/gretelai/gretel-synthetics for synthetic data generation (currently supporting GAN and RNN-based language models), and also our recent announcement around a Tabular LLM we're training to help people build with data https://gretel.ai/tabular-llm
A few areas where we've found tabular or Large Data Models to be really useful are:
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Libraries for synthetic data?
you can try QuantGAN: https://github.com/PakAndrey/QuantGANforRisk also try DoppelGANger https://github.com/gretelai/gretel-synthetics/tree/master/src/gretel_synthetics/timeseries_dgan
- Which open source tool for generating synthetic data sets?
- Gretel-synthetics: open-source library to create synthetic datasets
ydata-synthetic
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Coding Wonderland: Contribute to YData Profiling and YData Synthetic in this Advent of Code
Send us your North ⭐️: "On the first day of Christmas, my true contributor gave to me..." a star in my GitHub tree! 🎵 If you love these projects too, star ydata-profiling or ydata-synthetic and let your friends know why you love it so much!
- ydata-synthetic: NEW Data - star count:1083.0
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I absolutely hate my internship
1: Try to work with what you have and augment your dataset (honestly, 10 points is crap)
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Assessing the Quality of Synthetic Data with Data-Centric AI
Data Quality is key for all applications and models, and LLMs are no exception :) I've been working on a small community project with synthetic data (https://github.com/ydataai/ydata-synthetic) using ydata-synthetic, and it really shows! Underrepresentation (category imbalance) and missing data are two of the main issues!
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SOMEBODY HELP ME!
The Data-Centric AI Community creates community projects from time to time and is probably willing to help you in your project.
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Help for Data Scientist position
Join nice data communities and start networking.
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How to become a beast in DS ?
You know what they say: "Tell me who your friends are, and I'll tell you who you are!". Hang out with DS beasts and learn from them :)
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Hey guys, I have a few questions
Interesting question! I think our AI/ML devs at the Data-Centric AI Community could have nice perspectives for your to decide :)
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Embarking on a Journey of 99 Data Science Projects - From Beginner to Expert
Sounds like an amazing journey! Feel free to add your projects on our awesome-python-for-data-science repo as you go! And in case you need a hand or feedback on the projects, we'll be happy to help at the Data-Centric AI Community.
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Data science problems
The best to do is to get started with end-to-end projects in a collaborative environment (somewhat approaching real-world settings). You may find some interesting resources in this GitHub repository. The Data-Centric AI Community actually has a nice support system for this.
What are some alternatives?
Copulas - A library to model multivariate data using copulas.
REaLTabFormer - A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.
gretel-python-client - The Gretel Python Client allows you to interact with the Gretel REST API.
rex-gym - OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
adversarial-robustness-toolbox - Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
Conditional-Sig-Wasserstein-GANs
CTGAN - Conditional GAN for generating synthetic tabular data.
pytorch-forecasting - Time series forecasting with PyTorch
AI-basketball-analysis - :basketball::robot::basketball: AI web app and API to analyze basketball shots and shooting pose.