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hugo-theme-shell

Hugo Shell theme : Terminal-like theme with selectable color schemes.

hugo-theme-terminal

A simple, retro theme for Hugo

hyperdesktop2

imag

imag - Text based personal information management suite

image-processing-from-scratch

This project contains some interesting image processing algorithms that were wrote in python and c++ from scratch.

instabot.py

Instagram bot. It works without instagram api, need only login and password. Write on python.

keepassxc

KeePassXC is a cross-platform community-driven port of the Windows application “Keepass Password Safe”.

latex-journey

Aprendendo LaTeX

laurae

Advanced High Performance Data Science Toolbox for R by Laurae

learning-docker-the-hard-way

:whale: Want to learn Docker? Check this out!

lgallindo.github.io

A web application to build your blog on GitHub

lits

Liver Tumor Segmentation and Data Generation

lits---liver-tumor-segmentation-challenge

LiTS - Liver Tumor Segmentation Challenge

lits-challenge

INFORMATION FOR THE LIVER TUMOR SEGMENTATION CHALLENGE

lits-liver-tumor

https://competitions.codalab.org/competitions/17094

liver-ct-segmentation-package

mlf-core prediction package for U-Net models trained on the LiTS dataset.

liver-tumor-deep-learning

Computed tomography (CT) can provide comprehensive information for the diagnosis and treat- ment of liver tumors. How to retain the original image information as much as possible is of great significance for the segmentation of liver tumors. In order to solve the problem of information loss when CT image is converted into gray image which can be recognized by computer, a liver tumor cascade windowing image prepossessing method is proposed. According to the CT value range of liver and tumor, a deep learning image segmentation algorithm is used to compare the segmentation preci- sion of different window ranges in the cascade network, and the best CT value range of liver. The region of interest (ROI) of the liver is extracted by the best CT value range window for tumor segmentation. Using this method, deep learning segmentation networks such as Fully Convolutional Network (FCN), Convolutional Net- work for biomedical images (U-Net) and Deep Residual Unet (Res-Unet) are used to evaluate the precision of Liver Tumor Segmentation Challenge (LiTS) dataset. The vertical comparison in different windows and the horizontal comparison in different networks verify that the influence of different CT value ranges on the segmen- tation precision of liver (including tumor area) can reach 3.5%, and the influence on tumor area is 2.72%−12.37%; Different networks can improve liver segmentation by 2.5%

lm_memorization

Training data extraction on GPT-2

lodown

locally download and prepare publicly-available microdata

logisim-evolution

Digital logic design tool and simulator

mal

mal - Make a Lisp

mal.workshop

Mal Workshop for LambdaConf 2016

mario-level-1

The first level of Super Mario Bros made with Python and Pygame.

marktext

📝A simple and elegant markdown editor, available for Linux, macOS and Windows.

mastering-shiny

Mastering Shiny: a book

meta_ps

Production and Consumption of APSR, BJPS, Perspectives, PS, and World Politics Articles

miccai-lits2017

liver segmentation using deep learning

microdadosbrasil

Reads most common Brazilian public microdata (CENSO, PNAD, etc) easy and fast

mitmethods2017_python

Repository for Jupyter notebooks, slides, codes and other materials from the MIT Political Science Methods Workshop on 3/17/2017 (Introduction to Python and Web Scraping).

mlforhealthlabpub

Machine Learning and Artificial Intelligence for Medicine.