GLib Iteration Methods Files

Status: Planning

Brought to you by: mirrorsh

The interactive file manager requires Javascript. Please enable it or use sftp or scp.
You may still browse the files here.

Name	Modified	Size	InfoDownloads / Week
giter	2001-04-26		0
Totals: 1 Item			0

Other Useful Business Software

Train ML Models With SQL You Already Know

BigQuery automates data prep, analysis, and predictions with built-in AI assistance.

Build and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.

Try Free

Custom VMs From 1 to 96 vCPUs With 99.95% Uptime

General-purpose, compute-optimized, or GPU/TPU-accelerated. Built to your exact specs.

Live migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.

Try Free

Train ML Models With SQL You Already Know

BigQuery automates data prep, analysis, and predictions with built-in AI assistance.

Build and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.

Try Free

Recommended Projects

Bayesian Methods for Hackers
An introduction to Bayesian methods + probabilistic programming
Brahmi - Indic Input Methods & OT fonts
Brahmi project aims at producing Java Input Methods and OpenType fonts for Indian languages. They are bundled with Brahmi - Word Processor which installs the fonts, Input Methods and font.properties files
PowerMock
Java framework that allows you to unit test code normally
UACMe
Defeating Windows user account control
Modular toolkit for Data Processing MDP
The Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.