convergence
There are 74 repositories under convergence topic.
lirios/fluid
:book: Library for QtQuick apps with Material Design
lirios/shell
:shell: Convergent shell for desktops, phones and tables built with QtQuick, Wayland and Material Design
convergencelabs/convergence-server
The Convergence Server
convergencelabs/convergence-client-javascript
The Convergence JavaScript Client
atharmohammad/Code-N-Collab
CodeNCollab is a Collaborative Website for developers and competitive coders who likes to code and discuss about topics , problems or issues , etc
convergencelabs/convergence-project
The project used for Convergence Project Management and Issue Reporting
convergencelabs/javascript-examples
Examples for the Convergence Real-time Collaboration Engine
YisenWang/dynamic_adv_training
Code for ICML2019 Paper "On the Convergence and Robustness of Adversarial Training"
bigtestjs/interactor
All BigTest development has moved to https://github.com/thefrontside/bigtest
katiesaund/hogwash
Three bacterial GWAS methods all rolled into one easy-to-use R package
quey-project/quey-ui
A truly cute QML toolkit for cross-platform application development
convergencelabs/code-editor-demo
A mock up of a code editor built using React, Ace, Flux, and Convergence
convergencelabs/convergence-admin-console
The Convergence Admin Console
convergencelabs/convergence-proto
The Convergence Client-Server Protocol
strapsh/strap
Bootstrap a machine with one command!
toebes/ciphers
Cipher Generators
ConOnRails/ConOnRails
An application for managing convention operations
ChaitanyaC22/Deep-RL-Project---Maximize-total-profits-earned-by-cab-driver
The goal of this project is to build an RL-based algorithm that can help cab drivers maximize their profits by improving their decision-making process on the field. Taking long-term profit as the goal, a method is proposed based on reinforcement learning to optimize taxi driving strategies for profit maximization. This optimization problem is formulated as a Markov Decision Process i.e. MDP.
initcron/ultimate-ansible-bootcamp
Ultimate Ansible Bootcamp by School of Devops
convergencelabs/mxgraph-demo
A demonstration of collaborative diagram editing using mxGraph and Convergence
convergencelabs/jointjs-utils
Utilities to make using JointJS with Convergence easy.
convergencelabs/mxgraph-adapter
An adapter between mxGraph and Convergence
Breakend/ValuePolicyIterationVariations
Experiments testing variants of Value and Policy iterations.
glider4/computepac
A Python math package for numerical analysis: root finding, iterative solvers & other algorithms. Bisection, Newton, Euler, RK2, RK4, Adams-Bashforth-Moulton, etc. Uses Python, NumPy, SymPy, pytest.
meanmachin3/gossip-protocol
Gossip protocol implementation in F#
convergencelabs/dom-utils
Utilities to bind a Convergence Model to the DOM.
ldn-softdev/Rpnn
Resilient backProp Neural Network
lokhande-vishnu/IITK_Finalyear_project
Optimal Convergence Rate in Deep Neural Networks using HJB equation
BooleanCube/collatz-conjecture
Research based around a simple yet fascinating repetitive piecewise function.
convergencelabs/convergence-cluster-seed
The Convergence Cluster Seed
convergencelabs/geo-sketch-demo
A GeoSpatial collaboraiton demo using React, MobX, and ArcGIS
flowstateeng/Coursera-Deep-Learning
A five-course specialization covering the foundations of Deep Learning, from building CNNs, RNNs & LSTMs to choosing model configurations & paramaters like Adam, Dropout, BatchNorm, Xavier/He initialization, and others.
glider4/numerical-analysis
Numerical analysis in standard Python including Bisection method and Newton-Raphson, then SymPy integration for generalization and convergence test.
mixed-farming/Univariate-Linear-Regression
Implementing the gradient descent algorithm from scratch to perform univariate linear regression to analyze the profit made by a bike sharing company.
Sarthak-Mohapatra/Building-Algorithm-from-scratch-for-prediction-of-Average-GPU-run-time-and-classifying-the-run-type.
As part of this project, I have developed algorithms from scratch using Gradient Descent method. The first algorithm developed will be used to predict the average GPU Run Time and the second algorithm will be used to classify a GPU run process as high or low time consuming process.