A guide for Mozilla's developers and data scientists to analyze and interpret the data gathered by our data collection systems.
Обновлено 2024-10-31 21:30:42 +03:00
🔬 A Ruby library for carefully refactoring critical paths.
Обновлено 2024-06-20 02:20:54 +03:00
Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural model with key phrase extraction on open domains we have created OpenKP: a dataset of over 150,000 documents with the most relevant keyphrases generated by expert annotation.
Обновлено 2023-06-12 21:21:58 +03:00
Machine Learning Patient Risk Analyzer Solution Accelerator is an end-to-end (E2E) healthcare app that leverages ML prediction models (e.g., Diabetes Mellitus (DM) patient 30-day re-admission, breast cancer risk, etc.) to demonstrate how these models can provide key insights for both physicians and patients. Patients can easily access their appointment and care history with infused cognitive services through a conversational interface. In addition to providing new insights for both doctors and patients, the app also provides the Data Scientist/IT Specialist with one-click experiences for registering and deploying a new or existing model to Azure Kubernetes Clusters, and best practices for maintaining these models through Azure MLOps.
Обновлено 2022-12-08 19:18:29 +03:00
FOST is a general forecasting tool, which demonstrate our experience and advanced technology in practical forecasting domains, including temporal, spatial-temporal and hierarchical forecasting. Current general forecasting tools (Gluon-TS by amazon, Prophet by facebook etc.) can not process and model structural graph data, especially in spatial domains, also those tools suffer from tradeoff between usability and accuracy. To address these challenges, we design and develop FOST and aims to empower engineers and data scientists to build high-accuracy and easy-usability forecasting tools.
Обновлено 2022-01-06 09:18:00 +03:00
Knowledge management where the data scientist is in control
Обновлено 2021-01-28 04:58:33 +03:00
Manage your resources such as service and data connections using YAML and get credential management along the way. It's for all of data scientists, data engineers and developers.
Обновлено 2020-02-05 14:30:57 +03:00
A next-generation curated knowledge sharing platform for data scientists and other technical professions.
Обновлено 2017-01-27 03:35:25 +03:00
Replaced by https://github.com/github/scientist
Обновлено 2014-11-18 01:59:10 +03:00