zonca

import sys

filename = sys.argv[1]
print(filename)
f = open(filename, "r").read()
lines = f.splitlines()
first, rest = lines[:10], lines[10:]
fixed_first = ["---", "layout: post"]

for line in first:

nelsonpecora

As Prisma 1 is feature-frozen and Prisma 2 is going in some directions (no generated SDL schemas, code-first) that might not fit our needs, I've started research on some alternatives.

Data Layer

Prisma 2

PROS:

• no separate server needed
• nested mutations (OpenCRUD)

reuben

import argparse
import deepspeech as ds
import numpy as np
import shlex
import subprocess
import sys

parser = argparse.ArgumentParser(description='DeepSpeech speech-to-text from microphone')
parser.add_argument('--model', required=True,
                    help='Path to the model (protocol buffer binary file)')

ygrenzinger

Summary of book "Clean Architecture" by Robert C. Martin

Uncle Bob, the well known author of Clean Code, is coming back to us with a new book called Clean Architecture which wants to take a larger view on how to create software.

Even if Clean Code is one of the major book around OOP and code design (mainly by presenting the SOLID principles), I was not totally impressed by the book.

Clean Architecture leaves me with the same feeling, even if it's pushing the development world to do better, has some good stories and present robust principles to build software.

The book is build around 34 chapters organised in chapters.

jkleint

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

arundasan91

Freshly brewed !

With the availability of huge amount of data for research and powerfull machines to run your code on, Machine Learning and Neural Networks is gaining their foot again and impacting us more than ever in our everyday lives. With huge players like Google opensourcing part of their Machine Learning systems like the TensorFlow software library for numerical computation, there are many options for someone interested in starting off with Machine Learning/Neural Nets to choose from. Caffe, a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and its contributors, comes to the play with a fresh cup of coffee.

Installation Instructions (Ubuntu 14 Trusty)

The following section is divided in to two parts. Caffe's documentation suggest

bmcfee

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

wickman

Python development using Pants

brian wickman - @wickman

[TOC]

Why use Pants for Python development?

Pants makes the manipulation and distribution of hermetically sealed Python environments