gnanam336 Goto Github PK

12factor

2048-ai

A simple AI for 2048

abmash

Web application automation based on the visible rendered output with Java.

acat

Assistive Context-Aware Toolkit

alexa-avs-sample-app

This project demonstrates how to access and test the Alexa Voice Service using a Java client (running on a Raspberry Pi), and a Node.js server.

algo

Set up a personal IPSEC VPN in the cloud

algo-oct-17

JavaScript Algorithm Challenge - October 9 through 16

algorithmvisualizer

Algorithm Visualizer

am-i-affected-by-meltdown

Proof-of-concept / Exploit / checks whether system is affected by Variant 3: rogue data cache load (CVE-2017-5754), a.k.a MELTDOWN.

amazinghack

Team TopCOder

androidnoprivacy

A sample app that demonstrates how to extract sensitive personal information silently

apollo-11

Original Apollo 11 Guidance Computer (AGC) source code for the command and lunar modules.

apollo-android

:pager: A strongly-typed, caching GraphQL client for Android, written in Java

auto

A collection of source code generators for Java.

automated-bug-triaging-to-developer

For popular software systems, the number of daily submitted bug reports is high. Triaging these incoming bugs is a time consuming task. Major part of bug triaging is the assignment of a bug report to a developer with the appropriate expertise, who can resolve/fix the bug without reassigning to some other developer. We present an approach to automatically suggest developers who have the appropriate expertise for handling a bug report based on the identified software component the bug may reside in, obtained from the short description of the bug report. Our work is first to examine the impact of multiple machine learning dimensions( classifiers and training history) along with the ranked list of developers for prediction accuracy in bug assignment. We validate our approach on Eclipse Bugzilla covering 2,868,000 bug reports consisting of 253 components. We demonstrate that our techniques can achieve up to 93% prediction accuracy in bug assignment and significantly reduce the aberrant assignment of bugs. We compared the prediction time for our dataset using various algorithms such as Naive Bayes Text Classifier, Multinomial Naive Bayes and Linear SVM. We arrived at a conclusion that SVM provides higher prediction time and less learning time.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

bcc

BCC - Tools for BPF-based Linux IO analysis, networking, monitoring, and more

beginners-day

Note books for TensorFlow & Deep Learning

bert

TensorFlow code and pre-trained models for BERT

bigmuddy-network-telemetry-stacks

A batteries-included docker-based collection of demo stacks adapting network streaming telemetry to common consumer formats.

billow

Query AWS data without API credentials. Don't wait for a response.

blockchain-programming

Links to online resources about cryptocurrency and decentralized application development

blog-microservices-config

bnns-cocoa-example

An example of a neural network trained by tensorflow and executed using BNNS

book

Meta-data and Makefile needed to build the book. Main starting point.

bovine

Building Operational Visibility Into (n) Environments

bug-triager-scikit

Bug Triager using scikit learn cuz I'm laaaaaaazy.

caffe

Caffe: a fast open framework for deep learning.

caffeonspark

chancery

Automate on Github pushes