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30-seconds-of-code

Curated collection of useful Javascript snippets that you can understand in 30 seconds or less.

advanced-cpp-codes

CPP-Codes THIS REPOSITORY CONTAINS SOME BASICS OF THE ADVANCED C++ TOPICS LIKE STL, TEMPLATE CLASSES, OPERATOR OVERLOADING, VECTORS, SETS, MAPS, FUNCTORS ETC. IN C++ . THEY CAN BE USED AS A REFERENCE FOR C++ ANYTIME. ALSO I'LL BE ADDING SOME MORE CODES IN A WHILE . THEY WILL BASICALLY COVER BASICS OF ADVANCED ALGORITHMS AND DATA STRUCTURES .

algo_ds

Repository for cool algorithms and Datastructures

algorithm-datastructure-for-interview

Algorithm & DS basics required for interview.

algowiki

Repository which contains links and resources on different topics of Computer Science.

android-image-cropper

Image Cropping Library for Android, optimized for Camera / Gallery.

android-open-project

A categorized collection of Android Open Source Projects, More powerful web version:

android_guides

Extensive Open-Source Guides for Android Developers

androidinterview-q-a

The top Internet companies android interview questions and answers

androidutilcode

:fire: Android developers should collect the following utils(updating).

ankoapidemo

This is sample code for AnKo rest API(A representational state transfer (REST) API is a way to provide compatibility between computer systems on the internet.) It's code is written in java as well as kotlin. Please have a look at Kotlin basics before reading this code.

associate-android-developer-certification

All the info and materials about the certification that I've collected so far

awesome

:sunglasses: Curated list of awesome lists

awesome-android-ui

A curated list of awesome Android UI/UX libraries

b-safe

blogsrlife

This is a Blog app. Tried to create some simple features like adding a photo etc.

bugbusters

BugBusters for esya

caloriemeter

It measures the calorie burnt by person while walking.... needs some improvements.

coding-interview-university

A complete computer science study plan to become a software engineer.

codingninjas

all codes of Data Structures in Java

cognitive-behavioural-chatbot

CHatbot which implements mood detection using face detection and based on the mood suggests songs through a music recommender system, hold basic interaction and small talk with the user and also provide services through integrated APIs

computerscience

Free technical resources for faculty, students, and Microsoft developer advocates for use in computer science learning forums.

cosmos

Algorithms that run our universe | Your personal library of every algorithm and data structure code that you will ever encounter

cpp-toolkit

C++ Complete Practice Environment to learn programming

depression-detect

Predicting depression from acoustic features of speech using a Convolutional Neural Network.

emotion-detection-in-videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

emotion-recognition-neural-networks

Emotion recognition using DNN with tensorflow

every-programmer-should-know

A collection of (mostly) technical things every software developer should know

firebasemlkit

The project is made to help android developers understand and implement machine learning(ML) Kit provided by Firebase 🔥 as one of its features

firebaseui-android

Optimized UI components for Firebase