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from google.cloud import speech
from google.cloud.speech import enums
from google.cloud.speech import types

def main():

    credentials_json = "/path/to/json/file"
    from google.oauth2 import service_account
    cred = service_account.Credentials.from_service_account_file(credentials_json)

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import datetime
now = datetime.datetime.now()

from datetime import timedelta
diff = datetime.timedelta(days=42)

future = now + diff
future.strftime("%m/%d/%Y")

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#/usr/bin/python

from __future__ import print_function
from __future__ import absolute_import

import inspect
import sys

def foo1():
    pass

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df <- data.frame(Foo=c("A","B","A","B","B","C"))
pt <- as.data.frame(table(df))

ggplot(pt,aes(x=df,y=Freq)) + geom_bar(stat="identity")

pt$dfOrdered <- reorder(pt$df,pt$Freq)
ggplot(pt,aes(x=dfOrdered,y=Freq)) + geom_bar(stat="identity")

ggplot(pt,aes(y=Freq,x=dfOrdered,fill=dfOrdered)) + geom_bar(stat="identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + theme(legend.position="none")

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virtualenv hb_venv
source ~/hb_venv/bin/activate
pip install hyperopt
pip install scikit-learn

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'''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/

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Table Of Content

Skip to the relevant sections if needed.

• 2-min tutorial to do it the quick-and-dirty-way
• Concepts for resolving Git conflicts
• Setting up different editors / tool for using git mergetool
  • Finding out what mergetool editors are supported
• mergetool simple code example for vimdiff
  • Resolving conflict from a Git pull
• Other great references and tutorials

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-- Max Jaderberg 4/9/13
-- GPU Effectiveness test

require 'torch'
require 'sys'
require 'nn'
require 'xlua'

cmd = torch.CmdLine()
cmd:text()

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*** Discard git merge:
git reset --hard HEAD

*** Push a new local branch to a remote Git repository and track it too
$ git checkout -b feature_branch_name
... edit files, add and commit ...
$ git push -u origin feature_branch_name

*** To clean-up dead local branches:
git remote prune origin

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# Be sure that you have installed libraries (if not - google it):
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import xgboost as xgb
from sklearn.preprocessing import LabelEncoder
import numpy as np
from sklearn.metrics import accuracy_score
from sklearn.metrics import mean_squared_error
from sklearn import cross_validation