jieeshen Goto Github PK

20200921abbviepart1

3dunetcnn

Keras 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

analyze-tremor-bradykinesia-pd

This repository contains Python code that can be used to build analytics to measure aspects of tremor and bradykinesia in patients with Parkinson's Disease. These analytics utilize accelerometer data from a wrist-worn wearable device.

beginners-pytorch-deep-learning

Repository for scripts and notebooks from the book: Beginner’s Guide to Using PyTorchfor Deep Learning

biobankaccelerometeranalysis

Extracting meaningful health information from large accelerometer datasets

biosppy

Biosignal Processing in Python

bypass-paywalls-chrome

Bypass Paywalls web browser extension for Chrome and Firefox.

chatdoctor

chemprop

Message Passing Neural Networks for Molecule Property Prediction

chemprop-intro

An introduction to machine learning for chemical property prediction

clinical-trial-parser

Library for converting clinical trial eligibility criteria to a machine-readable format.

cnn_design_for_ad

Code for NeuraIPS2019 ML4H "On the design of convolutional neural networks for automatic detection of Alzheimer's disease"

contrastive-predictive-coding

Keras implementation of Representation Learning with Contrastive Predictive Coding

dbtk

deepchem

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

deeplearningfortimeseriesforecasting

A tutorial demonstrating how to implement deep learning models for time series forecasting

df_dataprocess

eda-explorer

Scripts to detect artifacts in EDA data

edkb_dataprocess

fdadbtk

fucking-algorithm

刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

gaitpy

grails-test

hacksdf

hacksdf_python

image-based-calorie-estimation

iris-recognition-py

Biometric identification based on human iris pattern comparison

jchem_logp

mealtagpoc

multivariate-time-series-imputation-with-generative-adversarial-networks

NIPS2018 paper