Copulas
ydata-synthetic
| Copulas | ydata-synthetic | |
|---|---|---|
| 1 | 60 | |
| 556 | 1,452 | |
| 0.4% | 1.5% | |
| 8.2 | 6.9 | |
| 19 days ago | 29 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.
Copulas
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[D] Has anyone used "copulas" before?
nice Python library for modeling with copulas that I've worked with: https://github.com/sdv-dev/Copulas
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?
CTGAN - Conditional GAN for generating synthetic tabular data.
REaLTabFormer - A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
DeepRL-TensorFlow2 - 🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2
SDV - Synthetic data generation for tabular data
gretel-python-client - The Gretel Python Client allows you to interact with the Gretel REST API.
gretel-synthetics - Synthetic data generators for structured and unstructured text, featuring differentially private learning.
Robotics-Object-Pose-Estimation - A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.
Conditional-Sig-Wasserstein-GANs
genalog - Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.
pytorch-forecasting - Time series forecasting with PyTorch