Manggala Pramuditya Wiryawan's repositories
Image-recognition
Image recognition methods from bag of words (BoW), Spatial Pyramid Matching (SPM), Sparse Codeing SPM (ScSPM) to convolutional neural networks (CNN)
tdd-with-springboot
Repo from SpringOne 2017 TDD talk
iak3-beginner-20173
This code is from my study group for my beginner class in Indonesia Android Kejar supported by Google Developer Group Indonesia.
awesome-deep-learning-papers
The most cited deep learning papers
Deep-Food
Python ML programm to classify food using Deep Learning and feature classifiers like SIFT, SURF with Bag of Words and SVMs
SimpleJavaDNN
A simple raw java implementation of a feed forward deep nerual network along with two demos of the XOR problem and a classification of The Iris Data Set.
Sunshine-IAK-Intermediate
Sunshine is an app that built in Android Studio for android mobile. This app is built for completing course in Udacity. You can acces that course through this link https://www.udacity.com/ud853
DBFlow
A blazing fast, powerful, and very simple ORM android database library that writes database code for you.
countries
An example Android app using Retrofit, Realm, Parceler, Dagger and the MVVM pattern with the data binding lib.
stetho-realm
Realm module for Stetho
laravel-debugbar
Laravel Debugbar (Integrates PHP Debug Bar)
api
A RESTful API package for the Laravel and Lumen frameworks.
Compare-OpenCV-SIFT-SURF-FAST-ORB
A multi threaded Python program runs OpenCV SIFT SURF FAST ORB
laravel-api-boilerplate-jwt
An API Boilerplate to create a ready-to-use REST API in seconds.
Natural-Image-Classification
This project was carried out using OpenCV Python and libraries such as numpy, matplotlib and scipy. It aims at classifying the images into three categories, namely, airplanes, cars and motorcycles using an Artificial Neural Network. Similar to humans, machines, too, require to analyze the features of an image to determine its content. Hence, for helping the system with the classification, we used algorithms such as Harris Corner Detection for detection of interest points in an image and consequently applied algorithms such as Histogram of Oriented Gradients (HoG), Speeded-Up Robust Features (SURF) and Daubechies D4 Wavelet Transform to generate the subsequent feature vectors. Our project utilized 160 out of the 500 images of Cars, 160 out of the 800 images of Motorcycles and 160 out of the 900 images of airplanes for training purposes. For the testing purposes, we selected 30 images from each of the categories which were not selected for the training. The system takes a test image as an input, classifies it into one of the three categories and annotates it.
python-neural-network
A neural network implementation using python. It supports variable size and number of hidden layers, uses numpy and scipy to implement feed-forward and back-propagation effeciently
example
Example Android Project
MySQL-CRUD-PHP-OOP
a CRUD class for MySQL using OOP in PHP