All Projects → makinarocks → Awesome Industrial Machine Datasets

makinarocks / Awesome Industrial Machine Datasets

Projects that are alternatives of or similar to Awesome Industrial Machine Datasets

Tutorial
Tutorial covering Open Source tools for Source Separation.
Stars: ✭ 223 (-0.89%)
Mutual labels:  jupyter-notebook
Ml From Scratch
机器学习算法 基于西瓜书以及《统计学习方法》,当然包括DL。
Stars: ✭ 225 (+0%)
Mutual labels:  jupyter-notebook
Tf Vqvae
Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).
Stars: ✭ 226 (+0.44%)
Mutual labels:  jupyter-notebook
Gan steerability
On the "steerability" of generative adversarial networks
Stars: ✭ 225 (+0%)
Mutual labels:  jupyter-notebook
Lstm Crf Medical
构建医疗实体识别的模型,包含词典和语料标注,基于python构建
Stars: ✭ 224 (-0.44%)
Mutual labels:  jupyter-notebook
Poretools
a toolkit for working with Oxford nanopore data
Stars: ✭ 225 (+0%)
Mutual labels:  jupyter-notebook
Sdc Vehicle Detection
Udacity Project - Vehicle Detection
Stars: ✭ 224 (-0.44%)
Mutual labels:  jupyter-notebook
Tensorflow notes
Tensorflow notes
Stars: ✭ 226 (+0.44%)
Mutual labels:  jupyter-notebook
Datascienceprojects
The code repository for projects and tutorials in R and Python that covers a variety of topics in data visualization, statistics sports analytics and general application of probability theory.
Stars: ✭ 223 (-0.89%)
Mutual labels:  jupyter-notebook
Text summarization with tensorflow
Implementation of a seq2seq model for summarization of textual data. Demonstrated on amazon reviews, github issues and news articles.
Stars: ✭ 226 (+0.44%)
Mutual labels:  jupyter-notebook
Deeplearning cv notes
📓 deepleaning and cv notes.
Stars: ✭ 223 (-0.89%)
Mutual labels:  jupyter-notebook
Attention network with keras
An example attention network with simple dataset.
Stars: ✭ 225 (+0%)
Mutual labels:  jupyter-notebook
Source separation
Deep learning based speech source separation using Pytorch
Stars: ✭ 226 (+0.44%)
Mutual labels:  jupyter-notebook
Rethinking Numpyro
Statistical Rethinking (2nd ed.) with NumPyro
Stars: ✭ 225 (+0%)
Mutual labels:  jupyter-notebook
Example Scripts
Example Machine Learning Scripts for Numerai's Tournament
Stars: ✭ 223 (-0.89%)
Mutual labels:  jupyter-notebook
Machinelearningwithpython
Starter files for Pluralsight course: Understanding Machine Learning with Python
Stars: ✭ 224 (-0.44%)
Mutual labels:  jupyter-notebook
Lrp toolbox
The LRP Toolbox provides simple and accessible stand-alone implementations of LRP for artificial neural networks supporting Matlab and Python. The Toolbox realizes LRP functionality for the Caffe Deep Learning Framework as an extension of Caffe source code published in 10/2015.
Stars: ✭ 225 (+0%)
Mutual labels:  jupyter-notebook
Theano Tutorial
A collection of tutorials on neural networks, using Theano
Stars: ✭ 226 (+0.44%)
Mutual labels:  jupyter-notebook
18s096
18.S096 three-week course at MIT
Stars: ✭ 226 (+0.44%)
Mutual labels:  jupyter-notebook
Set transformer
Pytorch implementation of set transformer
Stars: ✭ 224 (-0.44%)
Mutual labels:  jupyter-notebook

Awesome Public Industrial Datasets

A list of awesome-public-datasets found in the industry and their descriptions are shown below. Clicking the link will take you to the data description page. The data and its description will be updated periodically.

Tags

  • Sector

  • Label

  • Time-series

  • Miscellaneous

  • Simulation

List of Datasets

Semicon

Chemical

  • Gas Sensor Array Drift: This archive contains 13910 measurements from 16 chemical sensors exposed to 6 different gases at various concentration levels.
  • Chemical Detection Platform: The dataset contains 18000 time-series recordings from a chemical detection platform at six different locations in a wind tunnel facility in response to ten high-priority chemical gaseous substances.
  • Dynamic Gas Mixtures: The data set contains the recordings of 16 chemical sensors exposed to two dynamic gas mixtures at varying concentrations. For each mixture, signals were acquired continuously during 12 hours.

Mechanical

Steel

Power

  • Appliance Energy: Experimental data used to create regression models of appliances energy use in a low energy building.

  • Combined Cycle Power Plant: Combined Cycle Power Plant over 6 years.

  • GREEND : GREEND is an energy dataset containing power measurements collected from multiple households in Austria and Italy. It provides detailed energy profiles on a per device basis with a sampling rate of 1 Hz.

  • Eco(Electricity Consumption & Occupancy) : The ECO data set is a comprehensive data set for non-intrusive load monitoring and occupancy detection research.

  • UK DALE dataset : This dataset records the power demand from five houses. In each house we record both the whole-house mains power demand every six seconds as well as power demand from individual appliances every six seconds. In three of the five houses (houses 1, 2 and 5) we also record the whole-house voltage and current at 16 kHz.

  • BLUED dataset : The dataset consists of voltage and current measurements for a single-family residence in the United States, sampled at 12 kHz for a whole week.

  • REDD: A Public Data Set for Energy Disaggregation Research: A freely available data set containing detailed power usage information from several homes, which is aimed at furthering research on energy disaggregation (the task of determining the component appliance contributions from an aggregated electricity signal)

Battery

Etc

  • Hill-Valley: This is NOT a manufacturing dataset, but looks good for testing pattern detection methods.

  • APS System Failures: The datasets' positive class consists of component failures for a specific component of the APS system. The negative class consists of trucks with failures for components not related to the APS.

Contributors

About MakinaRocks

http://www.makinarocks.ai/

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].