ggchang17's Stars
nvbn/thefuck
Magnificent app which corrects your previous console command.
cleanlab/cleanlab
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
pyro-ppl/pyro
Deep universal probabilistic programming with Python and PyTorch
py-why/dowhy
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
dair-ai/ML-Course-Notes
🎓 Sharing machine learning course / lecture notes.
timeseriesAI/tsai
Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
dair-ai/Mathematics-for-ML
🧮 A collection of resources to learn mathematics for machine learning
mindee/doctr
docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.
jphall663/awesome-machine-learning-interpretability
A curated list of awesome responsible machine learning resources.
dair-ai/ML-Notebooks
:fire: Machine Learning Notebooks
SeldonIO/alibi-detect
Algorithms for outlier, adversarial and drift detection
b7leung/MLE-Flashcards
200+ detailed flashcards useful for reviewing topics in machine learning, computer vision, and computer science.
benedekrozemberczki/awesome-fraud-detection-papers
A curated list of data mining papers about fraud detection.
jacopotagliabue/you-dont-need-a-bigger-boat
An end-to-end implementation of intent prediction with Metaflow and other cool tools
altdeep/causalML
The open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalML
jphall663/interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
fabsig/GPBoost
Combining tree-boosting with Gaussian process and mixed effects models
patrickmineault/codebook
The Good Research Code Handbook
xiaohk/stickyland
Break the linear presentation of Jupyter Notebooks with sticky cells!
leehanchung/awesome-full-stack-machine-learning-courses
Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.
JanPalasek/pretty-jupyter
Creates dynamic html report from jupyter notebook.
label-sleuth/label-sleuth
Open source no-code system for text annotation and building of text classifiers
interpretml/gam-changer
Editing machine learning models to reflect human knowledge and values
ChangWeiTan/MultiRocket
Multiple pooling operators and transformations for fast and effective time series classification
jacopotagliabue/FREE_7773
Materials for my 2021 NYU class on NLP and ML Systems (Master of Engineering).
aws-samples/amazon-textract-transformer-pipeline
Post-process Amazon Textract results with Hugging Face transformer models for document understanding
UBC-DSCI/reproducible-and-trustworthy-workflows-for-data-science
jphall663/secure_ML_ideas
Practical ideas on securing machine learning models
o-P-o/disagree
Visualise, evaluate, and manage annotated data
srepho/srepho.github.io