bryanlimy

def main(): 
	# get the hashtags in string form
	string = input("Please paste your hashtags: ")

	# remove spaces and convert string to list of hashtags
	hashtags = string.split()

	# convert list to lowercase
	hashtags = [hashtag.lower() for hashtag in hashtags]

bryanlimy

import cv2 as cv

def convert(filename):
	img = cv.imread(filename)
	gray = cv.cvtColor(img, cv.COLOR_RGB2GRAY)
	return cv.imwrite(filename, gray)

if __name__ == '__main__':
	filename = raw_input('filename: ')
	if filename:

bryanlimy

Studying for a Tech Interview Sucks, so Here's a Cheat Sheet to Help

This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that Computer Science courses you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.

Data Structure Basics

Array

Definition:

• Stores data elements based on an sequential, most commonly 0 based, index.
• Based on tuples from set theory.

bryanlimy

def scaled_dot_product_attention(query, key, value, mask):
  matmul_qk = tf.matmul(query, key, transpose_b=True)

  depth = tf.cast(tf.shape(key)[-1], tf.float32)
  logits = matmul_qk / tf.math.sqrt(depth)

  # add the mask zero out padding tokens.
  if mask is not None:
    logits += (mask * -1e9)

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class PositionalEncoding(tf.keras.layers.Layer):

  def __init__(self, position, d_model):
    super(PositionalEncoding, self).__init__()
    self.pos_encoding = self.positional_encoding(position, d_model)

  def get_angles(self, position, i, d_model):
    angles = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(d_model, tf.float32))
    return position * angles

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def encoder_layer(units, d_model, num_heads, dropout, name="encoder_layer"):
  inputs = tf.keras.Input(shape=(None, d_model), name="inputs")
  padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask")

  attention = MultiHeadAttention(
      d_model, num_heads, name="attention")({
          'query': inputs,
          'key': inputs,
          'value': inputs,
          'mask': padding_mask

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def encoder(vocab_size,
            num_layers,
            units,
            d_model,
            num_heads,
            dropout,
            name="encoder"):
  inputs = tf.keras.Input(shape=(None,), name="inputs")
  padding_mask = tf.keras.Input(shape=(1, 1, None), name="padding_mask")

bryanlimy

def decoder_layer(units, d_model, num_heads, dropout, name="decoder_layer"):
  inputs = tf.keras.Input(shape=(None, d_model), name="inputs")
  enc_outputs = tf.keras.Input(shape=(None, d_model), name="encoder_outputs")
  look_ahead_mask = tf.keras.Input(
      shape=(1, None, None), name="look_ahead_mask")
  padding_mask = tf.keras.Input(shape=(1, 1, None), name='padding_mask')

  attention1 = MultiHeadAttention(
      d_model, num_heads, name="attention_1")(inputs={
          'query': inputs,

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def transformer(vocab_size,
                num_layers,
                units,
                d_model,
                num_heads,
                dropout,
                name="transformer"):
  inputs = tf.keras.Input(shape=(None,), name="inputs")
  dec_inputs = tf.keras.Input(shape=(None,), name="dec_inputs")

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NUM_LAYERS = 2
D_MODEL = 256
NUM_HEADS = 8
UNITS = 512
DROPOUT = 0.1

model = transformer(
    vocab_size=VOCAB_SIZE,
    num_layers=NUM_LAYERS,
    units=UNITS,