bryanlimy

1. Edit sources.list

• sudo vim /etc/apt/sources.list
• Uncomment all deb-src

2. Update and build-dep

• sudo apt-get update
• sudo apt-get build-dep radeontop

3. Install dependencies

• sudo apt-get install libxcb-dri2-0-dev

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

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

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

This README demonstrate how to install Nvidia driver, CUDA 9.0, cuDNN 7.2 on Ubuntu 18.04 computer with iGPU and Nvidia GPU. Using the following installation --flags, X Server is going to be running on the iGPU so that the dGPU can be used for CUDA tasks.

1. Install required packages

sudo apt install build-essential libxml2 gcc-multilib dkms gcc-6 g++6 freeglut3-dev libx11-dev libxmu-dev libxi-dev libglu1-mesa libglu1-mesa-dev

2. Add the following to /etc/modprobe.d/blacklist-nouveau.conf to disable nouveau

blacklist nouveau

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)

bryanlimy

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

  def __init__(self, d_model, num_heads, name="multi_head_attention"):
    super(MultiHeadAttention, self).__init__(name=name)
    self.num_heads = num_heads
    self.d_model = d_model

    assert d_model % self.num_heads == 0

    self.depth = d_model // self.num_heads

bryanlimy

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

bryanlimy

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

bryanlimy

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")