zhujunsan

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

1. Generate a ssh key

mchirico

#!/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():

joepie91

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.

kylemcdonald

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()))

shagunsodhani

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.

mmmikael

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

kastnerkyle

# 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 gael.varoquaux@normalesup.org
# 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

yrevar

{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',

dannguyen

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

mehdidc

"""
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.