Mehdi Cherti mehdidc

What / Why

Deploy key is a SSH key set in your repo to grant client read-only (as well as r/w, if you want) access to your repo.

As the name says, its primary function is to be used in the deploy process in replace of username/password, where only read access is needed. Therefore keep the repo safe from the attack, in case the server side is fallen.

How to

Generate a ssh key

Don't use VPN services.

No, seriously, don't. You're probably reading this because you've asked what VPN service to use, and this is the answer.

Note: The content in this post does not apply to using VPN for their intended purpose; that is, as a virtual private (internal) network. It only applies to using it as a glorified proxy, which is what every third-party "VPN provider" does.

A Russian translation of this article can be found here, contributed by Timur Demin.
A Turkish translation can be found here, contributed by agyild.
There's also this article about VPN services, which is honestly better written (and has more cat pictures!) than my article.

A Few Useful Things to Know about Machine Learning

The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks.

1. Learning = Representation + Evaluation + Optimization

All machine learning algorithms have three components:

Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt.
Evaluation function tells how good the machine learning model is.
Optimisation is the method to search for the most optimal learning model.

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

	#!/usr/bin/env python
	import tensorflow as tf
	import numpy as np
	from numpy import genfromtxt

	# Build Example Data is CSV format, but use Iris data
	from sklearn import datasets
	from sklearn.model_selection import train_test_split
	import sklearn
	def buildDataFromIris():

	import PIL.Image
	from cStringIO import StringIO
	import IPython.display
	import numpy as np
	def showarray(a, fmt='png'):
	a = np.uint8(a)
	f = StringIO()
	PIL.Image.fromarray(a).save(f, fmt)
	IPython.display.display(IPython.display.Image(data=f.getvalue()))

	from __future__ import absolute_import
	from __future__ import print_function
	import numpy as np
	np.random.seed(1337) # for reproducibility

	import random
	from keras.datasets import mnist
	from keras.models import Sequential
	from keras.layers.core import *
	from keras.optimizers import SGD, RMSprop

	# Author: Kyle Kastner
	# License: BSD 3-Clause
	# Implementing http://mnemstudio.org/path-finding-q-learning-tutorial.htm
	# Q-learning formula from http://sarvagyavaish.github.io/FlappyBirdRL/
	# Visualization based on code from Gael Varoquaux [email protected]
	# http://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html

	import numpy as np
	import matplotlib.pyplot as plt
	from matplotlib.collections import LineCollection

	{0: 'tench, Tinca tinca',
	1: 'goldfish, Carassius auratus',
	2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
	3: 'tiger shark, Galeocerdo cuvieri',
	4: 'hammerhead, hammerhead shark',
	5: 'electric ray, crampfish, numbfish, torpedo',
	6: 'stingray',
	7: 'cock',
	8: 'hen',
	9: 'ostrich, Struthio camelus',

	"""
	Implementation of 'Maximum Likelihood Estimation of Intrinsic Dimension' by Elizaveta Levina and Peter J. Bickel

	how to use
	----------

	The goal is to estimate intrinsic dimensionality of data, the estimation of dimensionality is scale dependent
	(depending on how much you zoom into the data distribution you can find different dimesionality), so they
	propose to average it over different scales, the interval of the scales [k1, k2] are the only parameters of the algorithm.