Code Monkey home page Code Monkey logo

  • šŸ‘‹ Hi, my name is Dan Betea (@danbetea). I am a research and data scientist with degrees in mathematics (PhD) and computer science (BS). On this page I'll post small pieces of code that might be useful to the (statistical/machine learning/mathematical/physical/programming) community; languages included are Julia, Mathematica, Python, C/C++, and perhaps others.

  • šŸ‘€ My current interests, in order, are:

    • machine/statistical learning;
    • data science;
    • adjacent subjects like simulations (e.g. Markov Chain Monte Carlo) and numerical methods therein;
    • probability and statistics, mathematical physics, (algebraic) combinatorics.

  • I am currently looking for new challenges in the industry. If you want to get in touch, see email below.

  • šŸ’žļø Iā€™m also always looking for collaborators on various aspects and projects from my interests listed above. Topics include:

    • regression and classification models;
    • transfer lerning (e.g. particular applications of neural style transfer to various fields);
    • simulations and numerical aspects of various models in statistical mechanics and physics and adjancent fields;
    • random sampling methods and Markov Chain Monte Carlo techniques.

  • šŸ“« How to reach me : firstName DOT lastName AT outlook DOT com.

Dan Betea's Projects

bessel_gumbel icon bessel_gumbel

Numerical check that the gap Fredholm determinant of a modified exponential coordinates Bessel(0) kernel coincides with the standard Gumbel distribution.

courgette-build icon courgette-build

Build script for Courgette, the binary patching tool used by Chrome

dezoomify-rs icon dezoomify-rs

Zoomable image downloader for Google Arts & Culture, Zoomify, IIIF, and others

dppy icon dppy

Python toolbox for sampling Determinantal Point Processes

neural-style-transfer icon neural-style-transfer

Keras Implementation of Neural Style Transfer from the paper "A Neural Algorithm of Artistic Style" (http://arxiv.org/abs/1508.06576) in Keras 2.0+

pytorch-neural-style-transfer icon pytorch-neural-style-transfer

Reconstruction of the original paper on neural style transfer (Gatys et al.). I've additionally included reconstruction scripts which allow you to reconstruct only the content or the style of the image - for better understanding of how NST works.

tracy_widom icon tracy_widom

Tracy--Widom GUE and GOE distributions in Mathematica using the Bornemann Fredholm method

tracywidom.jl icon tracywidom.jl

Tracy--Widom soft-edge distributions in Julia using Bornemann's method

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    šŸ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. šŸ“ŠšŸ“ˆšŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ā¤ļø Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.