oneAPI Data Analytics Library (oneDAL)
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Updated
May 29, 2024 - C++
oneAPI Data Analytics Library (oneDAL)
oneAPI Collective Communications Library (oneCCL)
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
Easily download all of your favorite Naughty images from multiple sites.
The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.
Client library to interact with various APIs used within Philips in a simple and uniform way
This neural network is designed to be able to take an 20px-by-20px gray-scale image and detect whether the input image contains either a rectangle or a circle.
This is a dataset intended to train a LLM model for a completely CVE focused input and output.
.Net Maui - Export all project files (.cs, .xaml ...) an single plain text file.
A tool to extract plain (unformatted) multilingual text, redirects, links and categories from wikipedia backups (dumps). Designed to prepare clean training data for AI training / Machine Learning software.
A tool for generating diverse synthetic training images using Bing Image Creator to facilitate the training of AI/ML image models.
LoRAdo is a UI that allows easy creation of LoRAs for stable diffussion
A Tool for Extracting Images from a Video for Artificial Intelligence Training.
A machine learning project that I worked on in Summer 2019 during my internship where I used MATLAB to train AlexNet to perform facial recognition in real-time to identify people. This was my first time using MATLAB.
A collection of 42 students' Core War Champions for AI training purposes
An "AI on-device" project for sequence model. Based at Tensorflow Lite for micro-controller, the model is created/trained/converted/flashed. At the end, an app is able to run, at SparkFun Edge Dev board, to recongnize speech although just words.
Sammlung von Paraphrasen zu platonischen Textstellen
An "AI-on-device" project walks with you through all necessary steps, from collecting your own data, creating and training your own Tensorflow model, generating your own Tensorflow-lite model, developing both Python and C++ programs to recognize images on Raspberry Pi 3.
Add a description, image, and links to the ai-training topic page so that developers can more easily learn about it.
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