dcai-lab
snorkel
| dcai-lab | snorkel | |
|---|---|---|
| 10 | 5 | |
| 400 | 5,719 | |
| 3.0% | 0.6% | |
| 5.4 | 5.2 | |
| 5 months ago | 13 days ago | |
| Jupyter Notebook | Python | |
| GNU Affero General Public License v3.0 | Apache License 2.0 |
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dcai-lab
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Resources to learn practical/industry-focused ML (preferably using TensorFlow)?
Data-Centric AI honestly if you've been working on ML pipelines this might be familiar to you
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Andrew NG, github courses
Another great resource inspired by the Andrew Ng data-centric AI movement is the Introduction to Data-Centric AI course taught this past semester at MIT by PhDs.
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Good Beginner Courses for ML?
Data-centric AI course. Brand new, taught the 1st time a few months ago by MIT PhD grads. This covers how to ensure good data quality for your models. More data science havy.
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
Thanks for the kind words! Make sure to check out the current open MIT course if you are just starting out: https://dcai.csail.mit.edu/
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The Missing Semester of Your CS Education
Introduction to Data-Centric AI https://dcai.csail.mit.edu
- Introduction to Data-Centric AI
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MIT Introduction to Data-Centric AI
Course homepage | Lecture videos on YouTube | Lab Assignments
snorkel
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
The paid product came out of an open source tool: https://github.com/snorkel-team/snorkel
- [Discussion] - "data sourcing will be more important than model building in the era of foundational model fine-tuning"
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Can't use load_data from utils
Actually, I referenced it in my issue as well. There seems to be different utils.py file in different folders under the snorkel-tutorials repo but the utils file we get after importing snorkel has a different [file](https://github.com/snorkel-team/snorkel/blob/master/snorkel/utils/core.py) ,i.e. the utils file is different in the main snorkel repo
- [D] A hand-picked selection of the best Python ML Libraries of 2021
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[Discussion] Methods for enhancing high-quality dataset A with low-quality dataset
Snorkel (https://github.com/snorkel-team/snorkel) might provide you exactly what you are looking for. From the docs:
What are some alternatives?
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
skweak - skweak: A software toolkit for weak supervision applied to NLP tasks
BotLibre - An open platform for artificial intelligence, chat bots, virtual agents, social media automation, and live chat automation.
argilla - Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
llm-course - Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
deodel - A mixed attributes predictive algorithm implemented in Python.
weasel - Weakly Supervised End-to-End Learning (NeurIPS 2021)
chordviz - A convolutional neural network trained using PyTorch to predict the next chord (as tablature) on a guitar based on image data. Includes labeling software for the image data as well as an iOS app for hosting and running the model.
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
UBB-INFO - All projects from university.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]