Search Results for "python feature selection"
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Showing 4 open source projects for "python feature selection"
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Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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GGMud
GGMud is a cross platform modern MUD client.
GGMud is an opensource multiplatform GRAPHICAL mud client. It uses the GTK toolkit for the GUI (quite similar to the ZMud one) and is compatible with tintin++ configuration files. -
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zopoclient
Scriptable GTK based Python MUD Client
...Alternatively, in script mode, the action text is interpreted as Python code. The mud, match objects and defined variables are accessible from any feature. For the more advanced user, the implementation is small and extensible. Specially the gtkmud/mud API is easy to understand. It is thread-safe enabling multiple threads to input data to the target MUD. Also, multiple listeners can register to get all MUD output. -
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py_mud is an extensible, (hopefully) user-friendly MUD client written entirely in cross-platform Python. It aims to be as feature-rich as commercial MUD clients (mushclient, ZMud), including support for MXP and inter-client chat (ZChat and MMChat).
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PySometric is an Isometric RPG written in Python. It uses PyGame for graphics, and will feature online play through a peer to peer network. Most of the programmers are learning how to program a 2-d game, and all new programmers are welcome.
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Cloud data warehouse to power your data-driven innovationBigQuery 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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