DerekChia

aaa
aarp
abb
abbott
abogado
ac
academy
accenture
accountant
accountants

DerekChia

### Locating UI elements ###

# By ID
<div id="coolestWidgetEvah">...</div>
element = driver.find_element_by_id("coolestWidgetEvah")
or
from selenium.webdriver.common.by import By
element = driver.find_element(by=By.ID, value="coolestWidgetEvah")

# By class name:

DerekChia

#!/usr/bin/env bash
# Reference : https://gist.github.com/ziadoz/3e8ab7e944d02fe872c3454d17af31a5#file-install-sh

# Versions (Update SELENIUM_STANDALONE_VERSION to the version of your choice)
CHROME_DRIVER_VERSION=`curl -sS chromedriver.storage.googleapis.com/LATEST_RELEASE`
SELENIUM_STANDALONE_VERSION=3.13.0
SELENIUM_SUBDIR=$(echo "$SELENIUM_STANDALONE_VERSION" | cut -d"." -f-2)

# Remove existing downloads and binaries so we can start from scratch.
sudo apt-get remove google-chrome-stable

DerekChia

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

def generate_dataset():
 x_batch = np.linspace(0, 2, 100)
 y_batch = 1.5 * x_batch + np.random.randn(*x_batch.shape) * 0.2 + 0.5
 return x_batch, y_batch

DerekChia

def linear_regression():
  x = tf.placeholder(tf.float32, shape=(None, ), name='x')
  y = tf.placeholder(tf.float32, shape=(None, ), name='y')

  with tf.variable_scope('lreg') as scope:
    w = tf.Variable(np.random.normal(), name='W')
    b = tf.Variable(np.random.normal(), name='b')
		
    y_pred = tf.add(tf.multiply(w, x), b)

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def run():
  x_batch, y_batch = generate_dataset()
  x, y, y_pred, loss = linear_regression()

  optimizer = tf.train.GradientDescentOptimizer(0.1)
  train_op = optimizer.minimize(loss)

  with tf.Session() as session:
    session.run(tf.global_variables_initializer())
    feed_dict = {x: x_batch, y: y_batch}

DerekChia

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

def generate_dataset():
	x_batch = np.linspace(0, 2, 100)
	y_batch = 1.5 * x_batch + np.random.randn(*x_batch.shape) * 0.2 + 0.5
	return x_batch, y_batch

def linear_regression():

DerekChia

a = tf.constant([10, 20])
b = tf.constant([1.0, 2.0])
# 'fetches' can be a singleton
v = session.run(a)
# v is the numpy array [10, 20]
# 'fetches' can be a list.
v = session.run([a, b])
# v is a Python list with 2 numpy arrays: the 1-D array [10, 20] and the
# 1-D array [1.0, 2.0]
# 'fetches' can be arbitrary lists, tuples, namedtuple, dicts:

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settings = {
	'window_size': 2	# context window +- center word
	'n': 10,		# dimensions of word embeddings, also refer to size of hidden layer
	'epochs': 50,		# number of training epochs
	'learning_rate': 0.01	# learning rate
}

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text = "natural language processing and machine learning is fun and exciting"

# Note the .lower() as upper and lowercase does not matter in our implementation
# [['natural', 'language', 'processing', 'and', 'machine', 'learning', 'is', 'fun', 'and', 'exciting']]
corpus = [[word.lower() for word in text.split()]]