Open Source FreeBSD Machine Learning Software
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Machine Learning Software for FreeBSD
Browse free open source Machine Learning software and projects for FreeBSD below. Use the toggles on the left to filter open source Machine Learning software by OS, license, language, programming language, and project status.
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Boost application security and continuity with SKUDONET ADC, our Open Source Load Balancer, that maximizes IT infrastructure flexibility. Additionally, save up to $470 K per incident with AI and SKUDONET solutions, further enhancing your organization’s risk management and cost-efficiency strategies.
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mlpy
Machine Learning Python
mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL. mlpy provides high-level functions and classes allowing, with few lines of code, the design of rich workflows for classification, regression, clustering and feature selection. mlpy is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3. mlpy is available both for Python >=2.6 and Python 3.X. -
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Bermuda Text-to-Speech
This project includes basic NLP and DSP techniques for Text-to-Speech
See TTS demo at: http://rslp.racai.ro/index.php?page=tts This is an entirely written in JAVA project which includes a set of tools and methods designed to enable Multilingual Text-to-Speech (TTS) synthesis. We currently support English and Romanian but we will soon train more models and make them available for download. If you want to read more about our other NLP and TTS tools check out http://nlptools.racai.ro. -
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R-Portfolio - breakeven optimally diversified investment portfolio. Оптимально диверсифицированный инвестиционный портфель.
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jPrediction
Open project ternary classifier of the machine learning
Open project ternary classifier of the machine learning -
BigQuery Studio provides a single, unified interface for all data practitioners of various coding skills to simplify analytics workflows from data ingestion and preparation to data exploration and visualization to ML model creation and use. It also allows you to use simple SQL to access Vertex AI foundational models directly inside BigQuery for text processing tasks, such as sentiment analysis, entity extraction, and many more without having to deal with specialized models.
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