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A game theoretic approach to explain the output of any machine learning model.
- interpretability
- machine-learning
- deep-learning
- gradient-boosting
Data and code behind the articles and graphics at FiveThirtyEight
- data
stable diffusion webui colab
- stable-diffusion
- stable-diffusion-web-ui
- stable-diffusion-webui
- colab
StableLM: Stability AI Language Models
This repository contains implementations and illustrative code to accompany DeepMind publications
Jupyter Interactive Notebook
- closember
- jupyter
- jupyter-notebook
- notebook
Kubernetes Community Documentation
The repository host gives no description.
Automatic extraction of relevant features from time series:
- data-science
- feature-extraction
- time-series
A scikit-learn compatible neural network library that wraps PyTorch
Create delightful software with Jupyter Notebooks
- jupyter-notebooks
- python
- literate-programming
- fastai
Debugging, monitoring and visualization for Python Machine Learning and Data Science
MIMIC Code Repository: Code shared by the research community for the MIMIC family of databases
- mimic-iii
- critical-care
- icu
- physionet
Open-source demos hosted on Dash Gallery
- plotly-dash
- dash
- plotly
Quantitative analysis, strategies and backtests
- quantitative-finance
- quantitative-trading
- portfolio-management
- risk-management
[Legacy] Data & AI Notebook templates catalog organized by tools, following the IMO (input, model, output) framework for easy usage and discovery..
- templates
- opensource
- naas
- notebooks-templates
The repository host gives no description.
A library for debugging/inspecting machine learning classifiers and explaining their predictions
TranslatedCAPTCHA recognition
- captcha-breaking
- keras-tutorials
- deep-learning
- keras
Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
- data-science
- python
- dag
- data-engineering
TensorFlow Tutorials
- tensorflow-tutorials
- convolutional-neural-networks
- recurrent-neural-networks
Algorithms for outlier, adversarial and drift detection
- anomaly
- outlier
- concept-drift
- detection
2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.