ankurhcu Goto Github PK
Name: ankur sharma
Type: User
Bio: I am a research scholar in NIT calicut
Name: ankur sharma
Type: User
Bio: I am a research scholar in NIT calicut
A simple, extensible Markov chain generator.
Examples of matplotlib codes and plots
Deep reinforcement learning for mobile edge computing
Dense Wireless Connectivity Datasets for the IoT.
A simulator for sketching mesh network routing strategies
MicroK8s is a small, fast, single-package Kubernetes for developers, IoT and edge.
Microservice Dependency Graph Dataset
Sample cloud-native application with 10 microservices showcasing Kubernetes, Istio, gRPC and OpenCensus.
A reference implementation demonstrating microservices architecture and best practices for Microsoft Azure
Sample project to create an application using microservices architecture
Run Kubernetes locally
Implementations for algorithms from lectures from MIT 6.006
Welcome to 6.86x Machine Learning with PythonβFrom 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.
A simple integer programming module that solves optimization problems using branch-and-bound
12 weeks, 24 lessons, classic Machine Learning for all
Machine Learning for Dynamic Resource Allocation in Network Function Virtualization
Machine Learning for Combinatorial Optimization - NeurIPS'21 competition
Implementation of CNN to recognize hand written digits (MNIST) running for 10 epochs. Accuracy: 98.99%
A data standard to enable communication between mobility companies and local governments.
Routing in Wireless Sensor Network using Multiobjective Particle Swarm Optimization
Intermodal Mobility Information System
Code for 'Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing', published in IEEE TPDS.
Source code for Neural Information Processing Systems (NeurIPS) 2018 paper "Multi-Task Learning as Multi-Objective Optimization"
Gateway to connect wireless MySensors network to MQTT broker
This is the repository for tracking the deadline and acceptance rate of top conferences for computer networks and communications.
A peper list for machine learning models solving combinatorial problems, NP-hard problems and problems in graphs.
NFVdeep: Deep Reinforcement Learning for Online Orchestration of Service Function Chains
Automated nginx proxy for Docker containers using docker-gen
π₯ π₯ The Open Source Airtable alternative - Powered by Vue.js π π
A declarative, efficient, and flexible JavaScript library for building user interfaces.
π Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. πππ
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google β€οΈ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.