Combine Python with machine learning principles to discover hidden patterns in raw data

Tens

A modular 4-channel E-Stim.

Implementation of the model "sketch-RNN" by google for generating sketches with a variational auto encoder

MK312 Communication Wrapper

Fruitify is a charming fruit recognition project that harnesses the power of TensorFlow, an advanced machine learning framework.

An E-Stim driver for the RP2040 microcontroller.

"新電性" Shin-Den-Sai is a Electrostimulation Machine Development Kit loosely based on the NeoDK

An Arduino Code that is integrated with a hardware model of a TENS Pressure Sensor Haptic Glove.

Assignments and final project for CMPT419/CMPT726

Simulating Deictic Pointing Gestures across Distances Using Electro Muscle Stimulation

Real-Estate Price Predictor is a machine learning project developed to assist users in estimating the price of real estate properties based on various features such as location, size, amenities, etc. The project utilizes supervised learning algorithms to train models on historical real-estate data and predicts prices for new properties.

BirdClassifier is a deep learning model designed to identify various bird species accurately. It leverages cutting-edge technology, integrating MobileNet-v2 and Convolutional Neural Network (CNN) architectures. MobileNet-v2 is chosen for its lightweight and efficient design, making it ideal for mobile applications.

In this project we will predict the cost required for a patient depending on his/her health conditions.

A 4-channel E-Stim.

Put the MK312 in connection with a Behringer X-Touch Mini

A Python-based solution leveraging OpenCV for real-time face mask detection. It utilizes computer vision techniques to detect and classify whether individuals in a video stream are wearing face masks accurately. The system provides visual indicators, enabling efficient monitoring and enforcement of mask-wearing protocols.

Electrotherapy device using ESP32

This repository showcases text generation using an RNN trained on Shakespeare's writings. The project aims to predict the subsequent character in a sequence based on input characters. The model can generate extended sequences of text through iterative calling.