ankurhcu Goto Github PK

markovify

A simple, extensible Markov chain generator.

matplotlib-gallery

Examples of matplotlib codes and plots

mec_drl

Deep reinforcement learning for mobile edge computing

mercator

Dense Wireless Connectivity Datasets for the IoT.

meshnetsimulator

A simulator for sketching mesh network routing strategies

metarl-offloading

microk8s

MicroK8s is a small, fast, single-package Kubernetes for developers, IoT and edge.

microservicedataset

Microservice Dependency Graph Dataset

microservices-demo

Sample cloud-native application with 10 microservices showcasing Kubernetes, Istio, gRPC and OpenCensus.

microservices-reference-implementation

A reference implementation demonstrating microservices architecture and best practices for Microsoft Azure

microservices-sample

Sample project to create an application using microservices architecture

minikube

Run Kubernetes locally

mit6.006

Implementations for algorithms from lectures from MIT 6.006

mitx_6.86x_machine_learning_with_python-from_linear_models_to_deep_learning_fall_2020

Welcome to 6.86x Machine Learning with Python–From Linear Models to Deep Learning. Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control. In this course, you will learn about principles and algorithms for turning training data into effective automated predictions. We will cover: Representation, over-fitting, regularization, generalization, VC dimension; Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning; On-line algorithms, support vector machines, and neural networks/deep learning. You will be able to: Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models Choose suitable models for different applications Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering You will implement and experiment with the algorithms in several Python projects designed for different practical applications. You will expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link these methods together, equipping you with the tools you need to develop new ones.

mixed-integer-linear-programming

A simple integer programming module that solves optimization problems using branch-and-bound

ml-for-beginners

12 weeks, 24 lessons, classic Machine Learning for all

ml-for-resource-allocation

Machine Learning for Dynamic Resource Allocation in Network Function Virtualization

ml4co-competition

Machine Learning for Combinatorial Optimization - NeurIPS'21 competition

mnist-digit.recognizer-cnns

Implementation of CNN to recognize hand written digits (MNIST) running for 10 epochs. Accuracy: 98.99%

mobility-data-specification

A data standard to enable communication between mobility companies and local governments.

mopso-wsn

Routing in Wireless Sensor Network using Multiobjective Particle Swarm Optimization

motis

Intermodal Mobility Information System

mtfl-for-personalised-dnns

Code for 'Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing', published in IEEE TPDS.

multiobjectiveoptimization

Source code for Neural Information Processing Systems (NeurIPS) 2018 paper "Multi-Task Learning as Multi-Objective Optimization"

mysensors_mqttgateway

Gateway to connect wireless MySensors network to MQTT broker

network-communication-conferences

This is the repository for tracking the deadline and acceptance rate of top conferences for computer networks and communications.

neural_combinatorial_optimization

A peper list for machine learning models solving combinatorial problems, NP-hard problems and problems in graphs.

nfvdeep

NFVdeep: Deep Reinforcement Learning for Online Orchestration of Service Function Chains

nginx-proxy

Automated nginx proxy for Docker containers using docker-gen

nocodb

🔥 🔥 The Open Source Airtable alternative - Powered by Vue.js 🚀 🚀