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followers: 10.0 following: 1.0 repos: 5.6K gists: 4.0

Name: Dimitri Grinkevich

Type: User

Company: Distributed Modeling Academy

Location: Kiev, Ukraine

Dimitri Grinkevich's Projects

fontforge

Free (libre) font editor for Windows, Mac OS X and GNU+Linux

This repository contains the code to generate predictions of critical violations at food establishments in Chicago. It also contains the results of an evaluation of the effectiveness of those predictions.

foodcomposition

R scripts to do analysis on food compositions

foolbox

Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, Keras, …

footballnotifier

A Python application that sends you a SMS when the football team you support scores.

foox

Creates species counterpoint using genetic algorithms (in Python).

for-ai.github.io

The source for our website.

forecast-demand-for-vending-machines

forecasting

Time Series Forecasting Best Practices & Examples

forecastnn

tools to easily build seq2seq and wavenet models on time series data

forestspy

spy on your random forests

forge

Source code for the Gluster Forge v2

formal-verification-intro

Presentation on formal verification of computer programs

formbot

Chatbot that uses Microsoft Bot Framework to create questionnaires based on XML templates.

foundation-dynamic-webpages-python

Source Code for 'Foundation Dynamic Web Pages with Python' by David Ashley

foundations-of-libvirt-development

Source Code for 'Foundations of Libvirt Development' by W. David Ashley

foundations-of-numerical-computing

Lecture Slides, Exercises, and Deployment Materials for "Foundations of Numerical Computing"

foundations-of-pygtk-development

Source Code for 'Foundations of PyGTK Development' by W. David Ashley and Andrew Krause

foundations_for_deep_learning

Building a foundation for deep learning with mathematics and neuroscience

fourier-discrepancies-cnn-detection

[CVPR 2021: Oral] In this work, we show that high frequency Fourier spectrum decay discrepancies are not inherent characteristics for existing CNN-based generative models.