Search Results for "training"
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Showing 15 open source projects for "training"
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Mathematics Dataset
This dataset code generates mathematical question and answer pairs
...The dataset enables models to learn mathematical problem-solving through examples that involve both numeric and symbolic reasoning. Version 1.0 includes over 2 million examples per category, with training splits labeled as “easy,” “medium,” and “hard,” supporting curriculum-based learning strategies. The data can be accessed via PyPI or generated locally using provided Python scripts, with outputs formatted for direct use in training or evaluation pipelines. -
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MathJax
Beautiful and accessible math in all browsers
A JavaScript display engine for mathematics that works in all browsers. No more setup for readers. It just works. MathJax provides tools to transform your content from traditional print sources into modern, accessible web content and ePubs. The MathJax team is available to train your staff in using our resources for preparing online teaching material and creating accessible STEM content. MathJax is highly flexible and can be tailored to the needs of your institution by creating customized... -
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NeuroNet
Neural network program. Creates and trains neural networks, shows data
Neural network program. Creates and trains neural networks, shows data. -
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The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Adaptive Gaussian Filtering
Machine learning with Gaussian kernels.
Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation routines. It is written in C++. -
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NDN Backprop Neural Net Trainer implements the backpropagation functionality subset of the open source NeuronDotNet object library in a generic user friendly application.
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DSOL
DSOL: A Distributed Simulation Object Library implemented in Java
...Since then, numerous papers and PhD dissertations have been developed, with applications in the fields of transportation and traffic, logistics, supply chain management, gaming and training, health care, and many others. -
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CvHMM
Discrete Hidden Markov Models based on OpenCV
This project (CvHMM) is an implementation of discrete Hidden Markov Models (HMM) based on OpenCV. It is simple to understand and simple to use. The Zip file contains one header for the implementation and one main.cpp file for a demonstration of how it works. Hope it becomes useful for your projects. -
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Nen
neural network implementation in java
3-layer neural network for regression and classification with sigmoid activation function and command line interface similar to LibSVM. Quick Start: "java -jar nen.jar" -
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PyCV is a Python package of modules useful for computer vision tasks. Its current focus is on boosting techniques, Haar-like features, and face detection. PyCV provides the world's fastest method for training a face detector, in a few hours.
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A tool for training your basic maths skills. Can be customised to allow you to build up your skills in any range of numbers, including multiplication from 1 to 3924 if you are so inclined.
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NeuroBox is an .NET OOP Library to generate, propagate and train complex neuronal networks with technologies like backpropagation with weight decay, momentum term, manhattan training, flatspot elimination etc.
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D-Cog (Declarative-Cognition) is a Java based framework for training software components (reusable, object-oriented, interface-driven components). Instead of programmed, software components are trained by example to get the expected results.
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Yawn: Yet Another W* Network: a java framework for developing, training and testing neural networks independently of the model and test environment.
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This is a conversion-to-binary training game prototype. Random bits appear, and you must shoot them where they fit onto various bytes (given in octal, hex or even bit ops, decimal or Ascii). Shoot wrongly and you lose that byte, or get it right and win.
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