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analyzing-vitevitch-network

balloon-finder

basic-web-server

Created a simple we server that handles one HTTP request at a time, and implements 200, 304, 400, 404 and 408 messages using socket programming. Extended the simple web server to a multi-threaded version, that is capable of handling multiple requests simultaneously

binary-search-tree

blast-them-away

bridging_languages

A development environment for the bridging_languages coding contest.

cmpt125

csp

csss-events-committee

Meeting Minutes and any Logo Designs Used

depth-of-field-calculator

depth-of-field-calculator-android

dr.napkin

Napkin acts as a mascot for the SFU Dodo Club. All commands have been coded by club members using Python and Python libraries

enhanced-discord.py

An enhanced discord.py, based off of the now-archived discord.py project

hangman

Hangman Game Created with Python3

kiwi

Kiwi acts as a mascot for the SFU Dodo Club. All commands have been coded by club members using Python and Python libraries

learning-java

Java Exercises

ling4100_project

english to IPA translator using openNMT to create the models

mario-baseball-chemistry

ocular

python-practice-database

randompage

restaurant-health-inspector-app

rrarar

sarbjotm

soluciones-klenberg

Algorithm Design (Kleinberg Tardos 2005) - Solutions

sudoku-solver-analysis

synthesize-swi-type-mri-scans

For this project, in a group of five we used Python and trained Adversarial Neural Networks so they could Synthesize SWI-Type MRI Scans The project aimed at reducing the time needed (and the cost) to perform an extensive MRI exam by generating MRI sequences from previously obtained scans of different types through a tailored deep learning model, namely an adversarial neural network. In more detail, the current project will synthesize the SWI sequence from T1w, T2w, and FLAIR. A successful application of the latter would allow medical staff to perform a lower number of sequences (reducing examination time), and to subsequently obtain the remaining needed sequences with software. The libraries used in the project were dominate, fnmatch, getopt, gzip, imageio, nibabel, numpy, os, pandas, pillow, pytorch, scipy, shutil, torchvision version 0.2.0, torchviz, tqdm, unet, and visdom. The deep learning model used for the purpose of this project is known as Multi-Modal Generative Adversarial Network (MM-GAN),

to-do-list

weather-app