🦉 Data Versioning and ML Experiments
- data-science
- machine-learning
- reproducibility
- data-version-control
Repository Index
47,133 repositories indexed
1–16 of 16 · Most stars
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
- machine-learning
- experiment-track
- deep-learning
- keras
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
Playing Pokemon Red with Reinforcement Learning
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
- python
- machine-learning
- infrastructure
- reproducible-research
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
- deep-learning
- machine-learning
- ml-platform
- ml-infrastructure
An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.
Hyperparameter Experiments with TensorFlow and Keras
- deep-learning
- keras
- keras-tensorflow
- hyperparameter-optimization
Code for all experiments.
Evidence-aware Japanese speech visualization: pitch and intonation, waveform inspection, and experimental alignment.
- audio-processing
- fastapi
- japanese
- librosa
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
Research experiments exploring uncommon quant techniques.
- analysis
- jupyter-notebook
- python
- quant
Enterprise-grade platform to generate and execute Cypress, Playwright, WebdriverIO, and Appium end-to-end tests from natural language requirements. (Appium is experimental and requires external mobile infrastructure.)
Code for the experiment proposed in Section 4.2 of the paper "The Bouncer Problem: Challenges to Remote Explainability".
Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.