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Created almost 4 years ago
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taDachs

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This is an article about using variational autoencoders for the generation of new data. It contains the code for generating the plots and training the mentioned models on celeb_a.

ros-nvim

ROS plugin for neovim

kit.vim

Colorscheme for vim using the KIT color palette

Vim Script

kardeshev

C++

propa-cheat-sheet

Shell

zettel.nvim

Zettelkasten plugin for neovim

Lua

GOme-of-life

Multiplayer Cellular Automata game

aoc-2022-lisp

Common Lisp

faxcoin

Messenger for people with taste.

Java

presentations

Random presentations for mostly kitcar stuff or as a conversation starter

TeX

locomotion-lecture-workspace

ROS Noetic workspace for the "Humanoid Robots: Locomotion and Whole Body Control" lecture

Dockerfile

Loco-Exercises

Python

mureg

Simple, unsurprisingly slow regex implementation.

Python

muros2cli

Attempt to bring the ROS1 cli tools like `rosed` and `rospkg` to ROS2

roguelike

C++

asfouriator

Extracts anki cards from IAR exams (tested so far only on WRT)

Python

Biomechanics-Lab-WS23

Jupyter Notebook

CV-template

TeX

muconf

Configuration library for Python

Python

Java-written-test

TeX

tce

A terrible chess engine in go

nort

Note managing script

Python

torch-experiments

Jupyter Notebook

taDachs/Overview-of-Variational-Autoencoder-based-Generative-Models

taDachs

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