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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.
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.
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 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.
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.
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.