dropout | Topic | Ecosyste.ms: Repos

Topic: "dropout"

kamranisg/Deeplearning.ai-Specialization

5 courses of Specialization in Deep Learning taught by Prof.Andrew Ng on Coursera

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WingsBrokenAngel/fractional_max_pooling_and_recurrent_convolutional_neural_network

Implementation of fractional max pooling model and recurrent convolutional neural network

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najeebkhan/sparseout

Sparseout: Controlling Sparsity in Deep Networks

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Digitally recognizing numbers in real life images has been a tough problem in artificial intelligence for many decades. The problem stems from the seemingly endless variations on fonts, colors, spacings, locations etc that these numbers can take within an image.

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srinadhu/convolutional_nn

Implemented fully-connected DNN of arbitrary depth with Batch Norm and Dropout, three-layer ConvNet with Spatial Batch Norm in NumPy. The update rules used for training are SGD, SGD+Momentum, RMSProp and Adam. Implemented three block ResNet in PyTorch, with 10 epochs of training achieves 73.60% accuracy on test set.

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dendisuhubdy/fraternal-dropout Fork of kondiz/fraternal-dropout

Fraternal Dropout (Research)

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sushant1827/LSTM_for_Household_Power_Consumption

This project explores the application of Long Short-Term Memory (LSTM) networks in predicting household power consumption. Using data collected at one-minute intervals, we demonstrate how LSTM can be leveraged for accurate forecasting.

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Ahmed-hassan-AI/nlp-Sentiment-Analysis

Sentiment Analysis

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NowyTeam/Tempo

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RimTouny/Weed-Species-Classification-and-Bounding-Box-Regression

Leveraging advanced image processing and deep learning, this project classifies plant images using a subset of the Plant Seedlings dataset. The dataset includes diverse plant species captured under varying conditions. This project holds significance within my Master's in Computer Vision at uOttawa (2023).

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iAmKankan/Regularization

Tutorial to handle Overfitting-Underfitting

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arjunsingh88/image_classification_cats_dogs

Image Classification problem, Cats v/s Dogs Model. Browse to https://imgclassification.herokuapp.com/ for the deployment via Heroku

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nadernamini/cs182-assignment1

CS 182 Spring 2019 - Assignment 1

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EPSOFT/Keras-Convolutianl-Networks

Keras Convolutianl Networks

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DorisDuan06/Stanford-CS231n-Convolutional-Neural-Networks-for-Visual-Recognition

cs231n assignments.

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samyak24jain/fifa-player-position-prediction

Predicting a FIFA player's playing position based on their skills using artificial neural networks.

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agiopouloskaptsikas/Dogs-vs.-Cats-An-Image-Classification-Task-using-TensorFlow

In this repository, I put into test my newly acquired Deep Learning skills in order to solve the Kaggle's famous Image Classification Problem, called "Dogs vs. Cats".

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ZJW-92/Fashion-Class-Classification

In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset.

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bmarroc/deep-learning

Jupyter notebooks implementing Deep Learning algorithms in Keras and Tensorflow

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ywkim92/Student-Dropout-Prediction2 Fork of iampratheesh/Student-Dropout-Prediction

Student dropout prediction: data originated from https://github.com/iampratheesh/Student-Dropout-Prediction

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tasptz/pytorch-stochastic-depth

Deep Networks with Stochastic Depth for PyTorch

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bhattbhavesh91/dropout-walkthrough

A repository to show how Dropout in Keras can Prevent Overfitting

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Manaliagarwal/Human-Activity-Recognition

Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions.

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thomastrg/DeepLearningPracticalWorks

Neural networks and deep learning practical works

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federicoarenasl/Regularization-techniques-on-NNs

During this study we will explore the different regularisation methods that can be used to address the problem of overfitting in a given Neural Network architecture, using the balanced EMNIST dataset.

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timolex/Radiodawg

Radiodawg Volumio webradio watchdog

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crosstherubicon/Backprop_Hyper-parameters

Understanding hyperparameters of neural network architectures using 3 cost functions, 3 activation functions, 2 regularizations and dropout.

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MathieuRita/MVA_BML_DropoutUncertainty

Project for the course Bayesian Machine Learning (MVA)

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chekoduadarsh/Random-Pixel-Drop

Language: Python - Size: 11.7 KB - Last synced at: 2 months ago - Pushed at: over 5 years ago - Stars: 1 - Forks: 0

shouryasimha/Ships-In-Satellite-images

Satellite imagery provides unique insights into various markets, including agriculture, defense and intelligence, energy, and finance. New commercial imagery providers, such as Planet, are using constellations of small satellites to capture images of the entire Earth every day. This flood of new imagery is outgrowing the ability for organizations to manually look at each image that gets captured, and there is a need for machine learning and computer vision algorithms to help automate the analysis process. The aim is to help address the difficult task of detecting the location of large ships in satellite images. Automating this process can be applied to many issues including monitoring port activity levels and supply chain analysis.

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SwikarGautam/DeepDenseNetwork

Fully connected neural network with Adam optimizer, L2 regularization, Batch normalization, and Dropout using only numpy

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sahildigikar15/MLP-Architetures-on-MNIST-dataset

Experimented with different architectures on MNIST dataset using MLPs with different dropouts.

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sahildigikar15/Different-CNN-Architectures-on-MNIST-dataset-

Experimented with different architectures and kernels on MNIST dataset using Convolutional Neural Networks.

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sanjeevai/PyTorchImageClassifier

First implemented the code in Jupyter Notebook and then converted it to a Python application that can be run from the command line

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sivaramakrishnan-rajaraman/Effect-of-Dropout-on-Model-Performance-toward-Skin-Cancer-Classification

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jeongwhanchoi/MLND-Capstone-Project

Capstone Project for Udacity Machine Learning Nanodegree

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somefunAgba/deeplearningWithMatlabinPy

Investigating the Behaviour of Deep Neural Networks for Classification

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cnavneet/notMNIST

Identifying text in images in different fonts using deep neural network techniques.

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jinglebot/Classifying_Traffic_Signs

Identify traffic sign images through Supervised Classification via Deep Learning and Computer Vision using Python, Tensorflow, Jupyter and Anaconda in AWS Cloud.

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bryant1410/dl-assignment2-cs231n

My solution to the 2nd assignment of UdelaR's Deep Learning course, based on Stanford CS231n.

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JaanuNan/DropGNN

🔬 DropGNN Implementation | Official PyTorch implementation of "DropGNN: Random Dropouts Increase the Expressiveness of GNNs". Enhances Graph Neural Networks with node dropout for improved drug discovery tasks.

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elaheghiyabi96/fashion_mnist_nn_torch

"Simple neural network model using Torch for classifying the Fashion MNIST dataset, implemented with Torch."

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mimihime0/CNN-Fashion-MNIST-Classifier

A convolutional neural network (CNN) for classifying the Fashion-MNIST dataset. Includes experiments with regularization techniques, data augmentation, and hyperparameter tuning to optimize model performance, achieving 89.76% test accuracy.

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vickshan001/CIFAR-10-CNN-Enhancer-Neural-Networks

CNN classifier for CIFAR-10 with enhanced architecture, dropout, and data augmentation.

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SedCore/FTDropBlock

Features-Time DropBlock (FT-DropBlock) regularization strategy for EEG-based CNNs.

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pngo1997/Fashion-MNIST-Classification-with-TensorFlow-Keras

Explores image classification using a Multi-layer Feed-Forward Neural Network on the Fashion MNIST dataset.

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arkanivasarkar/Deep-Learning-from-Scratch

Implementation of a Fully Connected Neural Network, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) from Scratch, using NumPy.

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harmanveer-2546/Retinal-Disease-Classification

The number of visually impaired people worldwide is estimated to be 2.2 billion, of whom at least 1 billion have a vision impairment that could have been prevented or is yet to be addressed. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment.

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VikentiosVitalis/image_and_video_analysis_and_technology

Laboratories - for 'Image and Video Analysis and Technology' M.Sc. Course ECE @ntua

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ishreya09/Skin-Cancer-Detection

Developed a CNN model to classify skin moles as benign or malignant using a balanced dataset from Kaggle, achieving a test accuracy of 81.82% and an AUC of 89.06%. Implemented data preprocessing by resizing images to 224x224 pixels and normalizing pixel values, enhancing model performance and stability.

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beenish-Ishtiaq/DEP-Task-4-Image-Classification-Cifar10

Developed a Convolutional Neural Network (CNN) model to classify images into 10 categories. The project includes data augmentation, model building, training, and evaluation.

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HarikrishnanK9/Tomato_Leaf_Disease_Detection

Tomato Leaf Disease Detection:Deep Learning Project

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harmanveer-2546/Diagnosis-Of-Pneumonia-By-CNN-Classifier

The primary objective s to develop an accurate and efficient classification model capable of identifying pneumonia cases in patients based on chest X-ray images. Pneumonia is a prevalent and potentially life-threatening respiratory infection. Early detection plays a critical role in timely intervention and effective treatment.

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abeed04/Sentiment-Analysis-using-Recurrent-Neural-Networks

Bidirectional RNNs are used to analyze the sentiment (positive, negative, neutral) of movie reviews. .

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chinmoyt03/Deep-Learning-Based-Diabetes-Risk-Analysis

Data Science Project: Comparing 3 Deep Learning Methods (CNN, LSTM, and Transfer Learning).

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shree-prada/Traffic-Signs-Recognition

This project is a real-time traffic sign recognition system built using Python, OpenCV, and a pre-trained CNN model, capable of detecting and recognizing traffic signs from images.

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parkjjoe/snn-aware-dropout

Develop SNN-aware Noise Addition Layers

Language: Python - Size: 71.3 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

hugohiraoka/Bank_Customer_Churn_Prediction

Model to predict bank customer churn

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vaibhavdangar09/Stock_Market_Prediction_And_Forecasting_Using_Bidirectional_LSTM_RNN

Utilizing advanced Bidirectional LSTM RNN technology, our project focuses on accurately predicting stock market trends. By analyzing historical data, our system learns intricate patterns to provide insightful forecasts. Investors gain a robust tool for informed decision-making in dynamic market conditions. With a streamlined interface, our solution

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manashpratim/Deep-Learning-From-Scratch

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Honolulu69/Successful-Aging

Machine learning Algorithms for the Prediction of Successful Aging in Older Adults

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shimazadeh/Neural_Networks

the implementation of a multilayer perceptron

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devanshkhare1705/Personalizing-K12-Education

Using deep learning to predict whether students can correctly answer diagnostic questions

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rochitasundar/Intro-To-Deep-Learning-With-PyTorch

This repository contains my code solutions to Udacity's coursework 'Intro to Deep Learning with PyTorch'.

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tanmay154agrawal/Classification-on-CIFAR10-using-Pytorch

This repository contains classification on CIFAR-10 using various activation functions and dropouts

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riccmon/Deep-Learning

Lab Sessions of the Deep Learning course - Master's degree in Artificial Intelligence at UniBo

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hwixley/EMNIST-NeuralNet-Regularisation-Experiments

A study of the problem of overfitting in deep neural networks, how it can be detected, and prevented using the EMNIST dataset. This was done by performing experiments with depth and width, dropout, L1 & L2 regularization, and Maxout networks.

Language: Jupyter Notebook - Size: 137 MB - Last synced at: almost 2 years ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

applesoju/DeepNeuralNetworks-P

Language: Python - Size: 726 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

Grafit24/DL-Framework-Numpy

Фреймворк глубоко обучения на Numpy, написанный с целью изучения того, как все работает под "капотом".

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beandkay/CheckerboardDropout

Code for Checkerboard Dropout paper

Language: Python - Size: 3.18 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

x1ew/Multi-class-Weather

This repository contains my project for computer vision.

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x1ew/Students-Academic-Performance

Deep learning Simple models

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Iamsdt/DeployBNDegit

A web app where user can draw Bengali digit and the AI model can detect handwritten digit and predict the digit.

Language: Python - Size: 11.1 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

AminKhavari78/Text-Generation-With-LSTM-Recurrent-Neural-Networks-in-Python-with-Keras

use LSTM model for text generation

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gugarosa/dropout_rbm

📄 Official implementation regarding the paper "Fine-Tuning Dropout Regularization in Energy-Based Deep Learning".

Language: Python - Size: 16.6 KB - Last synced at: 8 months ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

fraunhofer-iais/wasserstein-dropout

Wasserstein dropout (W-dropout) is a novel technique to quantify uncertainty in regression networks. It is fully non-parametric and yields accurate uncertainty estimates - even under data shifts.

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Arshpreet-Singh-1/ai-machine-learning-for-coders

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prakHr/Multiclass-Category-Classification

Contains notebooks that does categorical classification of shop items using embeddings in CNNs and Pyspark(Logistic Regression and MLlib)

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abhinavthapper31/waste-classification-CNN-Image-Augmentation

A model to classify images of waste products as Organic or Recyclable. Applied Image Augmentation to images and used basic CNN to classify images using Keras. Analysed the performance using Tensorboard. Detected over fitting using metric curves (accuracy) and addressed it using Dropout Regularization.

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hwixley/MLP-coursework1-report

Machine Learning Practical - Coursework 1 Report: a study of the problem of overfitting in deep neural networks, how it can be detected, and prevented using the EMNIST dataset. This was done by performing experiments with depth and width, dropout, L1 & L2 regularization, and Maxout networks.

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lovesaroha/Learning-Neural-Networks

Various concepts of neural networks applied in python (numpy) to help people get started with AI.

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MattMoony/convnet_mnist

Simple convolutional neural network (purely numpy) to classify the original MNIST dataset. My first project with a convnet. 🖼

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NeerajHazarika/kaggle-intro-to-deeplearning

This is a report on what are the things that I have learned from the Kaggle course intro to deep learning.

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KwokHing/TF2-Cifar10-CNN-Demo

Demo on performing multiclass image classification using Convolutional Neural Network (CNN) in Tensorflow 2. Techniques such as earlystopping, batchnormalizing and dropout are explored to prevent overfitting

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lauracarpaciu/Bees-vs-Wasps

Distinguish bees from wasps

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naoki-vn634/MCDropout

Implementation of Monte Carlo Dropout for Bayesian Convolutional Neural Network, Investigating Uncertainty of DeepNeuralNetwork

Language: Python - Size: 3.58 MB - Last synced at: over 1 year ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 1

parthatom/DNNs

Robust Neural Network Implementation with Logging models, Ensembles, Early Stopping, and much more.

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vi-hna-ja/kaggle-digit-recognition

MNIST is the de facto “hello world” dataset of computer vision. In this competition, our goal is to correctly identify digits from a dataset of handwritten images.

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TrilokiDA/Hand_Sign_Language_Recognition

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TrilokiDA/Employee-Retention

Figuring Out Which Employees May Quit

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Palak-15/Satelite_Image_Processing

It is tensorflow 2.0 implementation on Eurosat Dataset. IT classfies different types of satelite images. Used transfer learning in end to reduce overfitting and increase accuracy.

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krishcy25/TimeSeriesModeling-Apple-Stock-Prediction

This repository focuses on building Time Series Model (Recurrent Neural Network- LSTM) to predict the stock price of the Apple.Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems that involves time series related events

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trajkd/Behavioral-Cloning

Behavioral Cloning (project 4 of 9 from Udacity Self-Driving Car Engineer Nanodegree)

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lumosity4tpj/a-series-of-dropout

Size: 123 KB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 0 - Forks: 0

nevoit/Deep-Neural-Network

Deep Neural Network that can classify images from the MNIST database

Language: Python - Size: 295 KB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 0 - Forks: 1

kzhai/Lasagne Fork of Lasagne/Lasagne 📦

Lightweight library to build and train neural networks in Theano

Language: Python - Size: 54.3 MB - Last synced at: about 2 years ago - Pushed at: about 5 years ago - Stars: 0 - Forks: 0