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Name: William C Grisaitis
Type: User
Name: William C Grisaitis
Type: User
data structures and algorithms (y'all know why)
An "awesome" list of project templates for data science projects in Python
A tutorial on Bayesian multilevel linear regression for analyzing grouped data
A Fully Bayesian Inference of Tumor Microenvironment composition and gene expression
Given a random process with only two possible outcomes and known chances of either outcome, what is the best way to predict the outcome? Perhaps obviously, the best method is simply always to predict the more probable outcome. But what about other methods, such as making a random guess each time according to the same probabilities of either outcome? In other words, if outcome "A" happens 60% of the time, then how often am I correct if I guess "A" only 60% of the time (and outcome "B" 40% of the time)? The answer is not 60%; indeed, it is less. Analytically, if Prob(outcome="A") = p, and you guess "A" only p*100% of the time, then you'll be correct only (p^2 + (1-p)^2) * 100% of the time. For any p other than 0.5, this method is less accurate than simply choosing "A" every time. This simple fact is demonstrated by this mini-monte carlo simulation.
A website and user system (Express/Backbone)
Scripts for evaluation of convolutional networks
follows `djangoles-tutorial.pdf`
My dotfiles managed as a git repo in $HOME
My macOS environment: zsh, Git, Visual Studio Code, etc.
A user-space file system for interacting with Google Cloud Storage
Riemannian Adaptive Optimization Methods with pytorch optim
Python library to access Gene Expression Omnibus Database (GEO)
The largest collection of useful .gitignore templates
Create useful .gitignore files for your project
Demo of import failure with React Native native modules
Documenting my learning of JavaScript
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Website and blog
The Julia Set Fractal generated and animated with julia. Benchmarked against Python.
Create beautiful, publication-quality books and documents from computational content.
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.