sling428's Stars
hustcxl/Deep-learning-in-PHM
Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction
tatsu-lab/stanford_alpaca
Code and documentation to train Stanford's Alpaca models, and generate the data.
aws-samples/semantic-search-with-amazon-opensearch
gm-spacagna/deep-ttf
Survival analsyis and time-to-failure predictive modeling using Weibull distributions and Recurrent Neural Networks in Keras
ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark
Source codes for the paper "Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study"
aws-samples/amazon-sagemaker-bert-pytorch
KalleBylin/tft_webapp
Temporal Fusion Transformer Webapp
o19s/elasticsearch-learning-to-rank
Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch
WenjieDu/PyPOTS
A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values
oseiskar/simdkalman
Python Kalman filters vectorized as Single Instruction, Multiple Data
mrahtz/sanger-machine-learning-workshop
Code for machine learning workshop given to Sanger Systems group
harvardnlp/sa-vae
aimclub/FEDOT
Automated modeling and machine learning framework FEDOT
schwxd/awesome-digital-twin
ugrceyln/Scientific-Articles
adalmia96/Cluster-Analysis
rguo12/awesome-causality-algorithms
An index of algorithms for learning causality with data
mingzhangPHD/Few-shot-Learning-for-Fault-Diagnosis
This repository is for the Few-shot Learning for the fault diagnosis of large industrial equipment.
khundman/telemanom
A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.
openvinotoolkit/anomalib
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
jaungiers/MvTAe-Multivariate-Temporal-Autoencoder
This code demonstrates a multi-branch deep neural network approach to tackling the problem of multivariate temporal sequence prediction by modelling a latent state vector representation of data windows through the use of a recurrent autoencoder and predictive model.
reml-lab/hetvae
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series
CyberZHG/CLRS
Some exercises and problems in Introduction to Algorithms 3rd edition.
CyberZHG/keras-self-attention
Attention mechanism for processing sequential data that considers the context for each timestamp.
jihoo-kim/Awesome-Generative-RecSys
A curated list of Generative Recommender Systems (Paper & Code)
juyongjiang/awesome-graph-self-supervised-learning-based-recommendation
A curated list of awesome graph & self-supervised-learning-based recommendation.
danielegrattarola/keras-gat
Keras implementation of the graph attention networks (GAT) by Veličković et al. (2017; https://arxiv.org/abs/1710.10903)
danielegrattarola/spektral
Graph Neural Networks with Keras and Tensorflow 2.
PML-UCF/pinn
Physics-informed neural networks package
guillaume-chevalier/seq2seq-signal-prediction
Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier