yaoxy2010

ad_examples

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

bcnn

3D Bayesian Convolutional Neural Network (BCNN) for Credible Geometric Uncertainty. Code for the paper: https://arxiv.org/abs/1910.10793

code-dynalearn

corresnet

Corresnet is a deep-learning-based image jitter correction method for synchrotron nano-resolution tomographic reconstruction.

DeepADoTS

Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

dive_into_deep_learning

✔️李沐 【动手学深度学习】课程学习笔记：使用pycharm编程，基于pytorch框架实现。

flow-forecast

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

GAN_Review

A Survey and Taxonomy of the Recent GANs Development，computer vision & time series

GP-RNN_UAI2019

Implementaion of Gaussian Process Recurrent Neural Networks developed in "Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks", Qi She, Anqi Wu, UAI2019

huotarim-xgboost-li-ion-batteries

Timeseries of lithium-ion battery packs

Inside-Deep-Learning

Inside Deep Learning: The math, the algorithms, the models

lithium-battery-ocv-fitting

磷酸铁锂电池OCV-SOC曲线拟合python程序

mcdn-3d-seg

Monte Carlo Dropout Network for 3D Image Segmentation

Metis

Metis is a learnware platform in the field of AIOps.

ML-For-Beginners

12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

MSc_Thesis_Predictive_Maintenance_Batteries

The GitHub repository accompanying the MSc Thesis at Esade written by Oliver Caspers regarding the topic “Predictive Maintenance for Lithium-Ion Batteries: Predicting the Remaining Useful Life (RUL) using Data-Driven Machine Learning based on Real-World Battery Datasets”.

MTBook

《机器翻译：基础与模型》肖桐 朱靖波 著 - Machine Translation: Foundations and Models

OnClass

Single cell typing based on cell ontology

PaddleX

PaddlePaddle End-to-End Development Toolkit（『飞桨』深度学习全流程开发工具）

pbdl-book

Welcome to the Physics-based Deep Learning Book (v0.1)

Prediction_of_battery

pytorch-ts

PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend

RNN-Time-series-Anomaly-Detection

RNN based Time-series Anomaly detector model implemented in Pytorch.

SOH-estimation

Lithium ion battery state of health estimation and remaining useful life prediction using ELM

synthetic-data-gan

Experimenting with generating synthetic data using ydata-synthetic

TEC-reduced-model

Code and data for the paper "Systematic derivation and validation of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods" by Brosa Planella et al. (2021).

ThinkPython2

LaTeX source and supporting code for Think Python, 2nd edition, by Allen Downey.

time_series_forecasting

TimeSeriesForecasting-DeepLearning

An experiemtal review on deep learning architectures for time series forecasting