Open solution to the Home Credit Default Risk challenge 🏡
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Updated
Jun 22, 2022 - Python
Open solution to the Home Credit Default Risk challenge 🏡
Open solution to the Mapping Challenge 🌎
Packaged version of ultralytics/yolov5 + many extra features
Open solution to the Toxic Comment Classification Challenge
Open solution to the TGS Salt Identification Challenge
Open solution to the Data Science Bowl 2018
Open solution to the Airbus Ship Detection Challenge
Open solution to the Santander Value Prediction Challenge 🐠
Easiest way of fine-tuning HuggingFace video classification models
Open solution to the Cdiscount’s Image Classification Challenge
This library is a location of the LegacyLogger for PyTorch Lightning.
Your favorite Python graph libraries, scalable and interoperable. Graph databases in memory, and familiar graph APIs for cloud databases.
Flexible and scalable template based on PyTorch Lightning + Hydra. Efficient workflow and reproducibility for rapid ML experiments.
Tiny ORM for graph databases: Neo4j, RedisGraph, AWS Neptune or Gremlin
Implementation of a simulated environment using python
This Guidance demonstrates how to create an intelligent manufacturing digital thread through a combination of knowledge graph and generative artificial intelligence (AI) technologies. A digital thread offers an integrated approach to combine disparate data sources across enterprise systems, increasing traceability, accessibility, collaboration.
Example of solving CIFAR-10 with Neptune.
Project is deprecated. Please go to neptune-client.
Example project with scikit-learn and neptune.
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