dcai-lab
dgl
| dcai-lab | dgl | |
|---|---|---|
| 10 | 4 | |
| 400 | 13,039 | |
| 3.0% | 1.0% | |
| 5.4 | 9.9 | |
| 5 months ago | 7 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
dgl
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
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Detecting Out-of-Distribution Datapoints via Embeddings or Predictions
For trees/graphs, you’ll want a neural net that can take these as inputs for which I’m not sure a standard library exists. One recommendation is to checkout dgl: https://github.com/dmlc/dgl
- Beyond Message Passing: A Physics-Inspired Paradigm for Graph Neural Networks
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[D] Convenient libs to use for new research project at the intersection of GNN and RL.
The best pkg for GCN - https://github.com/dmlc/dgl
What are some alternatives?
snorkel - A system for quickly generating training data with weak supervision
pytorch_geometric - Graph Neural Network Library for PyTorch
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
BotLibre - An open platform for artificial intelligence, chat bots, virtual agents, social media automation, and live chat automation.
torchdrug - A powerful and flexible machine learning platform for drug discovery
llm-course - Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
spektral - Graph Neural Networks with Keras and Tensorflow 2.
deodel - A mixed attributes predictive algorithm implemented in Python.
deep_gcns_torch - Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org
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.
SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)