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A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.
- notebooks
- python
- data-science
- machine-learning
🦉 Data Versioning and ML Experiments
- data-science
- machine-learning
- reproducibility
- data-version-control
Image augmentation for machine learning experiments.
- image-augmentation
- machine-learning
- augmentation
- deep-learning
Tool for real-time visualization, monitoring and collaborative analysis of AI/ML experiments and live data. Supports Python, PyTorch/Torch, NumPy, TensorFlow/Keras https://visdom.dev
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
- python
- machine-learning
- infrastructure
- reproducible-research
Hyperparameter Experiments with TensorFlow and Keras
- deep-learning
- keras
- keras-tensorflow
- hyperparameter-optimization
learn code with tensorflow
- tensorflow-experiments
- gan
- infogan
- deeplearning
FOREST is a research agent for long-horizon science, where evidence gets lost, errors compound, and goals drift. It explores ideas, reads literature, forms hypotheses, runs real experiments, analyzes results, revises paths, and builds a full paper—all in an editable graph you can inspect, branch, redirect, with claims and figures linked to evidence
- ai-agents
- ai-scientist
- automated-research
- autoresearch
Code for all experiments.
TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524
- self-attention
- compound-protein-interaction
- transformercpi
- structure-free-virtual-sreening
A/B testing for Wagtail
- wagtail
- ab-testing
Python application to setup and run streaming (contextual) bandit experiments.
- streaming
- bandit
- mab
- cmab
My MicroPython experiments
A venv for coding agents: persistent isolated environments, parallel experiments, and shared checks.