Repository Index

Fast and Accurate ML in 3 Lines of Code

• automl
• machine-learning
• data-science
• deep-learning

Deep universal probabilistic programming with Python and PyTorch

• python
• pytorch
• machine-learning
• bayesian

Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.

• python
• ai
• data-science
• data-visualization

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

• cntk
• visualization
• tensorflow
• model-converter

ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.

• mlops
• machine-learning
• data-science
• production-ready

Scalable and user friendly neural :brain: forecasting algorithms.

• deep-learning
• forecasting
• esrnn
• nbeats

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

• machine-learning
• ml
• model-validation
• data-validation

Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

• python
• genetic-algorithm
• optimization
• numpy

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.

• deep-learning
• protein-sequences
• protein-structure
• semi-supervised-learning

Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

• pandas
• pandas-summary
• dataframes
• data-science

Code for PaperRobot: Incremental Draft Generation of Scientific Ideas

• pytorch
• memory-networks
• generation
• end-to-end-learning

AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.

• deep-learning
• agriculture
• pytorch
• dataset

Deep and Machine Learning for Microscopy

• microscopy
• materials
• machine-learning
• materials-science

MCP server for spatial transcriptomics analysis through natural language interfaces.

• bioinformatics
• cell-communication
• deconvolution
• machine-learning