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100-days-of-ml-code

100 Days of ML Coding

100daysofmlcode

2018-machinelearning-lectures-esa

Machine Learning Lectures at the European Space Agency (ESA) in 2018

aashiana

NewWorld

aco

Ant Colony based Clustering using MATLAB

adacomp

adaptivemodulationml

Adaptive Modulation using k-NN classification for OFDM system

airdrawjs

Draw on Screen using Webcam , Built using JS No GPU or Pre-request needed. Easy to integrate on your own apps.

algorithms-tour

How data science is woven into the fabric of Stitch Fix

animated-archer

Octave/Matlab Implementation for Job Shop Scheduling Problem

anomalydetectiontoolbox

A collection of algorithms for anomaly detection

aodv-gui-in-wsn

This code is for a MATLAB GUI in which AODV routing protocol is implemented for WSN. The source nodes are changing each time with number of packets.

aoi_rl

Reinforcement learning based scheduling algorithm for optimizing AoI in URLLC networks

app-specific-ann-based-feature-extractor

Algorithms to identify common dept store commodities using Computer Vision & Neural Networks implemented in MATLAB

asl_to_english

Reads your hand signs and translates them to English words using Tensorflow object detection API

attack-and-anomaly-detection-in-iot-sensors-in-iot-sites-using-machine-learning-approaches

Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.

audionetwork

Data transmission over sound waves written in JavaScript without any dependencies. All you need is just microphone, speakers and the browser!

authentication-and-autorization-in-graphql-server-with-node.js

Authentication and Autorization in GraphQL-Server with Node.js

awesome-ai

A curated list of artificial intelligence resources (Courses, Tools, App, Open Source Project)

awesome-bigdata

A curated list of awesome big data frameworks, ressources and other awesomeness.

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

awesome-edge-computing

A curated list of awesome edge computing, including Frameworks, Simulators, Tools, etc.

awesome-federated-computing

:books: :eyeglasses: A collection of research papers, codes, tutorials and blogs on Federated Computing/Learning.

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

awesome-time-series

Resources for working with time series and sequence data

bac-com-for-6g-ummtc

On the Application of BAC-NOMA to 6G umMTC

backdoors101

Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct your research on backdoors.

basic

Agent Based Simulation platform for CityScope

battery-lifetime-optimisation-of-wsn

This project uses A* search algorithm to optimize the average battery lifetime of a network of wireless sensor nodes

befl

Code for "Lightweight Blockchain-Empowered Secure and Efficient Federated Edge Learning", IEEE Transactions on Computers