zainulabidin302 Goto Github PK
Name: Zain Ul Abidin
Type: User
Company: @MachineLearningReply
Bio: AWS | Data Engineering
Location: Remote
Name: Zain Ul Abidin
Type: User
Company: @MachineLearningReply
Bio: AWS | Data Engineering
Location: Remote
A C++ Ant Colony Optimization (ACO) algorithm for the traveling salesman problem.
An active learning framework, using interchangeable algorithms and sample selection functions, including experimental results on a toy data-set.
Amazon Load Balancer Demo Using Terraform
Assignment Management System is a System for managing Academic Assignments
Assignment management system
HTML enhanced for web apps
AAAA
HTML template aroma by @divanraj
asignment-3-node-masterclass
Pirple Master Class Assignment 4
This a twitter app written in backbonejs with firebase at backend for my own testing
This is a boilerplate for starting a project with bison or yacc.
Card Game Bluf
Cause HTML Template React Implementation
Parametric models, and particularly neural networks, require weight initialization as a starting point for gradient-based optimization. In most current practices, this is accomplished by using some form of random initialization. Instead, recent work shows that a specific initial parameter set can be learned from a population of tasks, i.e., dataset and target variable for supervised learning tasks. Using this initial parameter set leads to faster convergence for new tasks (model-agnostic meta-learning). Currently, methods for learning model initializations are limited to a population of tasks sharing the same schema, i.e., the same number, order, type and semantics of predictor and target variables. In this paper, we address the problem of meta-learning parameter initialization across tasks with different schemas, i.e., if the number of predictors varies across tasks, while they still share some variables. We propose Chameleon, a model that learns to align different predictor schemas to a common representation. We use permutations and masks of the predictors of the training tasks at hand. In experiments on real-life data sets, we show that Chameleon successfully can learn parameter initializations across tasks with different schemas providing a 26\% lift on accuracy on average over random initialization and of 5\% over a state-of-the-art method for fixed-schema learning model initializations. To the best of our knowledge, our paper is the first work on the problem of learning model initialization across tasks with different schemas.
Sample circleci
Extract email and contact information from commoncrawl index
Compress and resize Width and Height of jpeg image with python script
This repository is for discussing of developing a community for computing.
Jupyter notebooks for Cryptographic Algorithms such ceaser, substitution, affine
A series of guides for an article at CSS-Tricks.com
dbt datapipline tutorial
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 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. 📊📈🎉
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 ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.