Stanza is a Python NLP Library for Many Human Languages. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python. A new collection of biomedical and clinical English model packages are now available, offering seamless experience for syntactic analysis and named entity recognition (NER) from biomedical literature text and clinical notes.

Stanza - A Python NLP Library for Many Human Languages

MindsDB enables you to use ML predictions in your database using SQL. MindsDB automates and abstracts machine learning models through virtual AI Tables. It can easily make predictions over very complex multivariate time-series data with high cardinality. 
<BR><BR>
Its features include:
<ul>
<li>Automatic data pre-processing, feature engineering and encoding</li>
<li>Classification, regression, time-series tasks</li>
<li>Bring models to production without &ldquo;traditional deployment&rdquo; as AI Tables</li>
<li>Get mModels&rsquo; accuracy scoring and confidence intervals for each prediction</li>
<li>Join ML models with existing data</li>
<li>Anomaly detection</li>
<li>Model explainability analysis</li>
<li>GPU support for models&rsquo; training</li>
<li>Open JSON-AI syntax to build models and bring your own ML blocks in a declarative way</li>
<li>REST API available as well</li>
</ul>

MindsDB enables you to use ML predictions in your database using SQL. MindsDB automates and abstracts machine learning models through virtual AI Tables. It can easily make predictions over very complex multivariate time-series data with high cardinality. 







MindsDB - In-Database Machine Learning



A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more. 

Machine-Learning / Deep-Learning / AI + Web3 -Tutorials

NFStream is a Python package providing fast, flexible, and expressive data structures designed to make working with online or offline network data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world network data analysis in Python. Additionally, it has the broader goal of becoming a common network data processing framework for researchers providing data reproducibility across experiments. NFStream extracts +90 flow features and can convert it directly to a pandas Dataframe or a CSV file.


NFStream - A Flexible Network Data Analysis Framework

Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects.
Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions, and collect more data to improve your model over time. Rubrix is designed to close this gap, enabling you to iterate as much as you need.

RUBRIX -  Python framework to explore, label, and monitor data for NLP

Discover open source projects across all platforms

Projects

Stanza - A Python NLP Library for Many Human Languages

MindsDB - In-Database Machine Learning

Machine-Learning / Deep-Learning / AI + Web3 -Tutorials

NFStream - A Flexible Network Data Analysis Framework

RUBRIX - Python framework to explore, label, and monitor data for NLP

Tagcloud

License

Suggested keywords:

Projects

Stanza - A Python NLP Library for Many Human Languages

MindsDB - In-Database Machine Learning

Machine-Learning / Deep-Learning / AI + Web3 -Tutorials

NFStream - A Flexible Network Data Analysis Framework

RUBRIX - Python framework to explore, label, and monitor data for NLP

Tagcloud

License