RhetTbull

""" Use Apple's Vision Framework via PyObjC to detect text in images 

To use:

python3 -m pip install pyobjc-core pyobjc-framework-Quartz pyobjc-framework-Vision wurlitzer

"""

import pathlib

lemenkov

Docker в продакшене.

С интересом ознакомился на днях с негативным опытом использования Docker в продакшене. Несмотря на то, что автор позиционирует себя, как матерого разработчика и девопса, легко отвечающего на вопросы типа "сколько шариков для пинг-понга могут взвесить в Сан-Франциско настройщики фортепиано, если шарики нельзя помечать", он не провел предварительного исследования, а спроектировав решение, сумел наткнуться на все известные минусы Docker. Видно, что его ограниченность одной убунчей, на которую он лазит по ssh со своего макбука, серьезно мешает ему в работе.

Укажу на то, что резануло глаз сразу.

"My first encounter with docker goes back to early 2015. Docker was experimented with to find out whether it could benefit us. At the time it wasn’t possible to run a container [i

metatoaster

# -*- coding: utf-8 -*-
# To use, at the directory you saved this:
# python -m unittest test_stdio

import sys
import io
import unittest


def stub_stdin(testcase_inst, inputs):

imalsogreg

## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!

# cuDNN acceleration switch (uncomment to build with cuDNN).
# USE_CUDNN := 1

# CPU-only switch (uncomment to build without GPU support).
CPU_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers

baraldilorenzo

##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

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

JBed

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization

#AlexNet with batch normalization in Keras 
#input image is 224x224

model = Sequential()
model.add(Convolution2D(64, 3, 11, 11, border_mode='full'))

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

n00neimp0rtant

## within current branch, squashes all commits that are ahead of master down into one
## useful if you merged with upstream in the middle of your commits (rebase could get very ugly if this is the case)
 
## commit any working changes on branch "mybranchname", then...
git checkout master
git checkout -b mybranchname_temp
git merge --squash mybranchname
git commit -am "Message describing all squashed commits"
git branch -m mybranchname mybranchname_unsquashed
git branch -m mybranchname

davfre

git cheat-sheet

The git command-line utility has plenty of inconsistencies http://steveko.wordpress.com/2012/02/24/10-things-i-hate-about-git/

A GUI like http://sourcetreeapp.com is often helpful, but staying on the command line usually quicker. This is a list of the commands I use most frequently, listed by functional category:

Current state

git status list which (unstaged) files have changed