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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.
Scalable and user friendly neural :brain: forecasting algorithms.
Python Toolkit for Causal and Probabilistic Reasoning
Modern, privacy-friendly, and detailed web analytics that works without cookies or JS.
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer
Python package for graph statistics
CNN visualization tool in TensorFlow
Interactive network visualization in Python and Dash, powered by Cytoscape.js
Visualization Package for NetworkX
Code for PaperRobot: Incremental Draft Generation of Scientific Ideas
[IROS 2020] se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
🎆 A visualization of the CapsNet layers to better understand how it works
InfluxDB Community Templates: Quickly collect & analyze time series data from a range of sources: Kubernetes, MySQL, Postgres, AWS, Nginx, Jenkins, and more.
Easily bring your Python applications online with the world's lightest GUI toolkit.
Kaapana is an open source toolkit for state of the art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. The name Kaapana comes from the Hawaiian word kaʻāpana, meaning "distributor" or "part".
Deep and Machine Learning for Microscopy