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applied-python-machine-learning-book

Applied Python Machine Learning Book

automl-tensorflow

A short and simple 3 layers Neural Network Tensorflow code to automatic machine learning

awesome-automl

Curating a list of AutoML-related research, tools, projects and other resources

awesome-scientific-python

A curated list of awesome scientific Python resources

axis_classification

back-propagation-neural-network-classification-algorithm-of-ecg-signal

In the ECG automatic classification system, the accuracy of classification has always been an important part of scholars'research. Therefore, BP neural network is used to classify and recognize ECG signals in ECG automatic classification system.

binary-spiking-neural-network

bmc

Notes on Scientific Computing for Biomechanics and Motor Control

brian2-simulator

brian_xor

Solution to XOR problem using spiking neural networks. Currtently working on a liquid state machine and training with ReSuMe rule.

browser-ai-arrhythmia-detection

Machine Learning based Arrythmia Classification in Browser only

catordogmachinelearningimageclassifierproject

Created and analyzed a machine learning project for determining if an image is a picture of a dog or of a cat. 1,170 images from the Kaggle data set were used for feature selection performed by a pre-trained Convolutional Neural Network to create a Cosine K-Nearest Neighbor classifier and a Cubic Support Vector Machine classifier. 25,000 images were then used for a second feature selection to create another Cubic Support Vector Machine since it was the better performing classifier. The entire machine learning workflow was performed including partitioning the data, feature selection, classifier selection, training, cross-validation and testing. The MATLAB Classification Learner App was used to help create the classifiers. This project was performed by two partners and myself for my Introduction to Artificial Intelligence elective course (CAP4630).

cell-based-nas-analysis

[ICLR'22] On Redundancy and Diversity in Cell-based Neural Architecture Search

cnn1d-ecg

ECG classification using 1d cnn

cnn2snn

cnn2snn-1

Will be putting my experiments on converting CNNs to Spiking Neural networks or SNNs

cnns-on-chb-mit

The project is about applying CNNs to EEG data from CHB-MIT to predict seizure

cse237d-pynq-snn-accelerator

Hardware implementation of Spiking Neural Network on a PYNQ-Z1 board

dac2018-tgiif

The 1st place winner's source codes for DAC 2018 System Design Contest, FPGA Track

darts-pt

[ICLR2021 Outstanding Paper] Rethinking Architecture Selection in Differentiable NAS

deepecg

A library for classifying single-lead ECG waveforms as either Normal Sinus Rhythm, Atrial Fibrillation, or Other Rhythm.

digits-recognition

a python program for digits predictions

dl_exercise

Deep Learning Exercise

ecg-acquisition-classification

Single Lead ECG signal Acquisition and Arrhythmia Classification using Deep Learning

ecg-af-detection-physionet-2017

AF Classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge 2017

ecg-analysis-mit-bih

I test different approaches to analysing electrocardiograms and test them on the MIT-BIH Arrhythmia Database.

ecg-arrhythmia-classification

In this project I have classified ECG into normal and non normal types of arrhythmia using VGG Net Architecture

ecg-atrial-fibrillation-classification-using-cnn

This is a CNN based model which aims to automatically classify the ECG signals of a normal patient vs. a patient with AF and has been trained to achieve up to 93.33% validation accuracy. The dataset details are given at the How to use section.

ecg-classification

Code for training and test machine learning classifiers on MIT-BIH Arrhyhtmia database

ecg-classification-1