yukoga

/* Polyfill indexOf. */
var indexOf;

if (typeof Array.prototype.indexOf === 'function') {
    indexOf = function (haystack, needle) {
        return haystack.indexOf(needle);
    };
} else {
    indexOf = function (haystack, needle) {
        var i = 0, length = haystack.length, idx = -1, found = false;

yukoga

-- Author: Krisjan Oldekamp
-- https://stacktonic.com/article/enrich-a-single-customer-view-with-google-analytics-4-big-query-data

declare lookback_window int64 default 365; -- how many days to lookback into the ga4 dataset to calculate profiles

-- udf: channel grouping (you could put this in a permanent function)
-- also see https://stacktonic.com/article/google-analytics-4-and-big-query-create-custom-channel-groupings-in-a-reusable-sql-function
create temporary function channel_grouping(tsource string, medium string, campaign string) as (
    case
        when (tsource = '(direct)' or tsource is null) 

yukoga

yukoga

yukoga

def publish(client, pubsub_topic, data_lines):
    """Publish to the given pubsub topic."""
    messages = []
    for line in data_lines:
        messages.append({'data': line})
    body = {'messages': messages}
    str_body = json.dumps(body)
    data = base64.urlsafe_b64encode(bytearray(str_body, 'utf8'))

    client.publish(topic=pubsub_topic, data=data)

yukoga

# User module to receive tweets
from recevie_tweets_pubsub import receive_tweets

import pandas
from bokeh.io import curdoc
from bokeh.models import ColumnDataSource
from bokeh.models import DatetimeTickFormatter
from bokeh.plotting import figure, output_file

import sys

yukoga

class DictionaryUtility:
    """
    Utility methods for dealing with dictionaries.
    """
    @staticmethod
    def to_object(item):
        """
        Convert a dictionary to an object (recursive).
        """
        def convert(item): 

yukoga

import numpy as np
import tensorflow as tf
from keras.callbacks import TensorBoard
from keras.layers import Input, Dense
from keras.models import Model


def write_log(callback, names, logs, batch_no):
    for name, value in zip(names, logs):
        summary = tf.Summary()

yukoga

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

yukoga

from keras.preprocessing.text import Tokenizer

# ベクトル化したい文章をリストで宣言します。
texts = ["I am a student. He is a student, too.", "She is not a student."]

# Tokenizerをインスタンス化し、上で用意した文章を与えます。
tokenizer = Tokenizer()
tokenizer.fit_on_texts(texts)

# 与えられた文章の数を取得します。