aqibsaeed/Place-Recognition-using-Autoencoders-and-NN

Stars
266
Rank 154,103 (Top 4 %)
Language
Jupyter Notebook
License
Apache License 2.0
Created about 8 years ago
Updated about 7 years ago

aqibsaeed/Place-Recognition-using-Autoencoders-and-NN

aqibsaeed

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Place recognition with WiFi fingerprints using Autoencoders and Neural Networks

Place recognition with WiFi fingerprints using Autoencoders and Neural Networks

Tensorflow implementation of model discussed in the following paper: Low-effort place recognition with WiFi fingerprints using deep learning

Tools Required

Python 3 is used during development and following libraries are required to run the code provided in the notebook:

Tensorflow
Numpy
Pandas

Dataset

The UJIIndoorLoc dataset used for model training and testing, can be downloaded from the following [link].

Note: If you see mistakes or want to suggest changes, please submit a pull request.

Multilabel-timeseries-classification-with-LSTM

Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks.

Jupyter Notebook

Urban-Sound-Classification

Urban sound classification using Deep Learning

Jupyter Notebook

Human-Activity-Recognition-using-CNN

Convolutional Neural Network for Human Activity Recognition in Tensorflow

Jupyter Notebook

Genetic-CNN

CNN architecture exploration using Genetic Algorithm

Anomaly-Detection

Anomaly detection algorithm implementation in Python

Jupyter Notebook

Estimation-of-Remaining-Useful-Life-using-CNN

Convolutional Neural Network based regression approach for estimating machinery's remaining useful life

Jupyter Notebook

Genetic-Algorithm-RNN

Using Genetic Algorithms to optimize Recurrent Neural Network's Configuration

Jupyter Notebook

On-device-activity-recognition

Personalized machine learning on the smartphone

Entity-Embedding-with-LSTM-for-Time-Series

Entity Embedding with LSTM for Time Series

Jupyter Notebook

Research-Paper-Categorization

Research paper classification using machine learning and NLP

Jupyter Notebook

Tensorflow-ML

Different machine learning algorithms implementation in Tensorflow

Jupyter Notebook

Sensor-Transformer

Transformer Network for Time-Series, Sensor and Wearable Data

Occupancy-Detection

Occupancy detection of an office room from light, temperature, humidity and CO2 measurements

Jupyter Notebook

Mental-Overload-Detection-RVS

Mental Overload Detection with Respiration Variability Spectrogram

Jupyter Notebook

Matlab-ML

Matlab/Octave implementation of various machine learning algorithms

CF-with-DAE-on-Market-Basket-Data

Collaborative Filtering with Denoising Autoencoder on Market Basket Data

Jupyter Notebook

Binary-Early-Exit-Network

Binary Early-Exit Network for Adaptive Inference on Low-Resource Devices

Jupyter Notebook

Elasticsearch-Client

Elasticsearch client in C# for quick experimentation