hmsh Goto Github PK

active-learning-bayesian-convolutional-neural-networks

Active Learning on Image Data using Bayesian ConvNets

activeharnet

ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition

bank_interview

:bank: 银行笔试面试经验分享及资料分享（help you pass the bank interview, and get a amazing bank offer!）

cvpr19_incremental_learning

Learning a Unified Classifier Incrementally via Rebalancing

dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

dropoutuncertaintyexps

Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"

educnblogs

This is a project to realize the mobile client for edu.cnblogs.com.

elis

Efficient Learning Interpretable Shapelets for Accurate Time Series Classification, ICDE 2018

firstmaven

firstmaweb

hello-world

New study

human-activity-recognition-with-neural-network-using-gyroscopic-and-accelerometer-variables

The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.

machine-learning

:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

machine-learning-in-action-python3

《机器学习实战》python3源码

machinelearning

My blogs and code for machine learning. http://cnblogs.com/pinard

monte_carlo_dropout

Uncertainty estimation in deep learning using monte carlo dropout with keras

testgit

testgit2