Base URL: http://translate.google.com/translate_tts
It converts written words into audio. It accepts GET
requests.
q
The query string to convert to audio
tl
Translation language, for example, ar
for Arabic, or en-us
for English
Base URL: http://translate.google.com/translate_tts
It converts written words into audio. It accepts GET
requests.
q
The query string to convert to audio
tl
Translation language, for example, ar
for Arabic, or en-us
for English
GNU Octave is a high-level interpreted language, primarily intended for numerical computations.
(via GNU Octave)
Hint: I also mad an octave docset for Dash: https://github.com/obstschale/octave-docset
Magic words:
psql -U postgres
Some interesting flags (to see all, use -h
or --help
depending on your psql version):
-E
: will describe the underlaying queries of the \
commands (cool for learning!)-l
: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)###Let's install Vagrant###
###Select a Vagrant Box from https://vagrantcloud.com###
#add it to your list of boxes
vagrant box add hashicorp/precise32
#create a new folder for your project & init vagrant
#! /usr/bin/env python | |
"""Git pre-commit hook to run pylint on python files. | |
To install: | |
wget https://gist.github.com/nivbend/7e0e306a98138916b3c9#file-run_pylint-py -O .git/hooks/pre-commit | |
""" | |
from __future__ import print_function | |
from subprocess import check_output, CalledProcessError | |
from sys import stderr |
# vim: set fileencoding=utf-8 : | |
# | |
# How to store and retrieve gzip-compressed objects in AWS S3 | |
########################################################################### | |
# | |
# Copyright 2015 Vince Veselosky and contributors | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at |
Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...
// see: https://github.com/chadoe/docker-cleanup-volumes
$ docker volume rm $(docker volume ls -qf dangling=true)
$ docker volume ls -qf dangling=true | xargs -r docker volume rm
'''This script goes along the blog post | |
"Building powerful image classification models using very little data" | |
from blog.keras.io. | |
It uses data that can be downloaded at: | |
https://www.kaggle.com/c/dogs-vs-cats/data | |
In our setup, we: | |
- created a data/ folder | |
- created train/ and validation/ subfolders inside data/ | |
- created cats/ and dogs/ subfolders inside train/ and validation/ | |
- put the cat pictures index 0-999 in data/train/cats |
# | |
# -------------------- authorize script -------------------- | |
# | |
# -*- coding: utf-8 -*- | |
""" | |
Just adjust the global variables accordingly and then run this script from the command line. | |
Make sure the correct client_secret_file is selected, | |
and that it has the necessary right configured. |
## install homebrew | |
echo "Installing Homebrew.." | |
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" | |
echo "Homebrew successfully installed" | |
## install brew cask | |
echo "Installing brew cask.." | |
brew tap homebrew/cask | |
echo "Homebrew cask successfully installed" |