Movie ratings prediction
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Updated
Dec 20, 2017 - Jupyter Notebook
Movie ratings prediction
This is the repo that will be used to store the code used for the Intel / IBACs AI technical workshop hosted at the University of Connecticut.
Set of 2D & 1D CNN models to classify images of handwritten numbers from the MNIST dataset using Keras.
Set of 1D CNN models to classify sound clips from the Urban Sound Classification dataset using Keras and Librosa
Predict blood pressure (systolic and diastolic) rythm from ECG and pulse oximeter signals
Anti-hydrogen detection using CNNs from ASACUSA experiment
[深度应用]·首届中国心电智能大赛初赛开源Baseline(基于Keras val_acc: 0.88)
用Tensorflow2.0進行外匯收盤價預測, 使用LSTM, 1d-CNN, Seq2Seq
1-dimensional convolution implementation using C++ and CUDA
In this work we propose two postprocessing approaches applying convolutional neural networks (CNNs) either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time domain approach follows an end-to-end fashion, while the cepstral domain approach uses analysis-synthesis with cepstral d…
1D convolutional neural networks for activity recognition in python.
Deep learning approach for estimation of Remaining Useful Life (RUL) of an engine
1-D convolution implementation using Python and CUDA
1DConvNet applied to room occupancy detection based on data from several environment sensors. Data courtesy of the UCI Machine Learning Repository.
ECG classification using public data and state-of-the-art 1D CNN models. This work is based on George Moody Challenge 2020
Model for translating speech to text. This is similar to using amazon alexa amongst many other products
A repository related to a master thesis in electronics, informatics and technology. Title: "Comparing Cardiological and Algorithm-Based ECG Interpretation in Athletes: Can Artificial Intelligence Improve the Algorithms?"
Play The Piano With Deep Learning 用深度学习弹钢琴 2019-5-22
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.
DeepFake ECG generator based on 1D Convoloutional Neural Networks
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