| commit | 610222cbca25b76c327cb5ace780c3d0ead9e1bf | [log] [tgz] |
|---|---|---|
| author | Sebastian Baunsgaard <baunsgaard@apache.org> | Tue Jan 30 19:34:33 2024 +0100 |
| committer | Sebastian Baunsgaard <baunsgaard@apache.org> | Wed Jan 31 10:57:05 2024 +0100 |
| tree | d0ac2975fa852a75a4ab1ab11dca5194d7d11c25 | |
| parent | 2cd782f72a1f767e67c14022384fa50d7161b540 [diff] |
[SYSTEMDS-3670] TSNE PCA preprocessing This commit adds a comment and example script of TSNE with PCA preprocessing According to Scikit Learn then PCA preprocessing reduces the dimensions TSNE has to work with and, therefore, improve performance. LDE Project Part 1 WS 2023/2024 Closes #1991
Overview: SystemDS is an open source ML system for the end-to-end data science lifecycle from data integration, cleaning, and feature engineering, over efficient, local and distributed ML model training, to deployment and serving. To this end, we aim to provide a stack of declarative languages with R-like syntax for (1) the different tasks of the data-science lifecycle, and (2) users with different expertise. These high-level scripts are compiled into hybrid execution plans of local, in-memory CPU and GPU operations, as well as distributed operations on Apache Spark. In contrast to existing systems - that either provide homogeneous tensors or 2D Datasets - and in order to serve the entire data science lifecycle, the underlying data model are DataTensors, i.e., tensors (multi-dimensional arrays) whose first dimension may have a heterogeneous and nested schema.
Quick Start Install, Quick Start and Hello World
Documentation: SystemDS Documentation
Python Documentation Python SystemDS Documentation
Issue Tracker Jira Dashboard
Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source
