Search Results for "self learning" - Page 14

prenu project aims to create an artificial intelligence capable of learning and development of behavioral patterns using self-created and modified python scripts

Library of classes allowing the construction of an array of Artificial Neural Networks such as the Multilayer Perceptron and Self-Organising Map. The aims are to produce very good and adaptable ANNs while serving as a comprehensible teaching/learning aid.

Self study flashcards-based tool. Represents knowledge as question/answer pairs. Uses a scheduling algorithm for inter repetition invtervals optimization. Employs Web standards (XML, XSLT, HTML). Allows learning history interchange with similiar programs.

Loacker is a fully self-contained .NET framework for building and managing e-learning applications, including a CMS engine, a persistent object store, a Web server with browser-based content editing and real-time audio/video conferencing capabilities.

There are lots of bots out for Half-Life. Many closed source or Windows only. All of them are based on Botmans Code ( http://www.planethalflife.com/botman ). The goal of this project is to make a self-learning, waypointless, opensource, Linux+Win. Bot

An easy to install and configure self-learning firewall based on ipchains

OpenGL based software dedicated to learn guitar by our own. Developed for Windows, Mac OS X and Linux

A self paced Microprocessor course for those with no Microprocessor experience but who wish to learn about and build devices with them. Starting with a early Microprocessor, the Intel 8085 and providing a Java based 8085 simulator and a PDF handbook the student may work thru assembly/machine code examples to gain understanding. Other on-line book links are provided. Finishing with a modern Micro-controller, the ARM Cortex-M0 based STM32F051.

...It is part of the Ornith 1.0 family, which includes dense and MoE models post-trained on Gemma 4 and Qwen 3.5. The model focuses on coding-agent performance across benchmarks such as Terminal-Bench, SWE-Bench, NL2Repo, OpenClaw, and ClawEval. Its training uses a self-improving reinforcement learning framework that optimizes not only solution attempts but also the scaffolds that guide those attempts, helping the model discover better search paths and produce higher-quality solutions. Ornith-1.0-397B is a reasoning model by default, generating <think> blocks before final answers, and supports tool calling through OpenAI-compatible endpoints. ...

Not having complete information on courses offered nearby of home may cause that the choice of place for training entails unnecessary journeys due to ignorance of the best offer. This service is focused on providing information about learning offer in the city, been the students the final users. The citizens will be able to find information about a wide calendar of courses, classified by sectors of activity, and will be able to find places and resources for self-learning (Libraries, foundations, schools, e-learning, and technological centres developing learning activities or promoting self-learning resources).

MiniMax-M2.7 is a large-scale open-weight language model designed for advanced agent-based workflows, professional software engineering, and complex productivity tasks. With 229B parameters, it introduces a self-evolution framework in which the model actively improves its own capabilities by updating memory, generating skills, and iterating through reinforcement learning experiments. This process enables it to autonomously refine systems, achieving measurable performance gains such as a 30% improvement in programming scaffolds. M2.7 excels in real-world engineering scenarios, including debugging, log analysis, system monitoring, and root cause investigation, demonstrating strong system-level reasoning comparable to SRE workflows. ...

wav2vec2-large-xlsr-53-portuguese is an automatic speech recognition (ASR) model fine-tuned on Portuguese using the Common Voice 6.1 dataset. It is based on Facebook’s wav2vec2-large-xlsr-53, a multilingual self-supervised learning model, and is optimized to transcribe Portuguese speech sampled at 16kHz. The model performs well without a language model, though adding one can improve word error rate (WER) and character error rate (CER). It achieves a WER of 11.3% (or 9.01% with LM) on Common Voice test data, demonstrating high accuracy for a single-language ASR model. ...