- Name: Ravikant Singh
- Organisation: OpenMined
- Project: Implement Fan-Vercauteren Homomorphic Encryption Scheme in PySyft
- Proposal: https://docs.google.com/document/d/14JrXJ9_ddB8Qm-RbnOnA2NL_GxXxKXei56dSJrVumR0/edit?usp=sharing
- Mentor: Ayoub Benaissa
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""" | |
Eisen EU COVID-19 challenge starter kit | |
NOTE: you need to register to the challenge, download and unpack the data in | |
order to be able to run the following example. | |
Find more info here: https://www.covid19challenge.eu | |
Information about Eisen can be found at http://eisen.ai -- Join the community on Slack https://bit.ly/2L7i6OL |
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// The core app code | |
var myApp = (function () { | |
'use strict'; | |
// Create a public methods object | |
var methods = {}; | |
/** | |
* Extend the public methods object |
Good documentation should include two distinct elements - a Guide and an API:
- The Guide builds concepts, providing examples, etc.
GitBook is well suited to
This is the code documented in https://pelson.github.io/2017/xkcd_font_merge_then_extract_glyphs/.
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git fetch upstream | |
git checkout master | |
git merge upstream/master | |
git merge upstream/master | |
git commit | |
git push |
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import os.path | |
import tensorflow as tf | |
from tensorflow.python.platform import gfile | |
import matplotlib.pyplot as plt | |
log_dir = "/tmp/inception_v3_log" | |
if not os.path.exists(log_dir): | |
os.makedirs(log_dir) |
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#!/usr/bin/python | |
# run the script: python git-find-big-files.py <the branch> <file size> | |
# Example use: python git-find-big-files.py fix-remove-files 1000000 (this value equals 1 Megabyte) | |
# the Fix-remove-files specifies the branch that you are cleaning | |
import os, sys | |
def getOutput(cmd): | |
return os.popen(cmd).read() |
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#!/usr/bin/env python3 | |
import os | |
import re | |
import numpy as np | |
from ast import literal_eval as make_tuple | |
from tkinter import * | |
from tkinter import ttk | |
from tkinter import filedialog |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
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