Mynuddin-dev

import cv2
import os  
import numpy as np
import csv
import pandas as pd

dir = "/content/drive/MyDrive/Applied_data_Science"
def absoluteFilePaths(directory):
  filePaths = []
  for dirpath,_,filenames in os.walk(directory):

Mynuddin-dev

HASKELL PROGRAMMING FROM FIRST PRINCIPLES

CHAPTER1. ALL YOU NEED IS LAMBDA

1.1 All You Need is Lambda

lambda calculus:

computation model, 1930s, Alonzo Church, formalizing a method, Turing machines

Mynuddin-dev

## Day 1: Report Repair
## Part 1
----------------------------

with open("data.txt") as f:
    numbers = f.readlines()
    # print(len(numbers))
    for i in range(len(numbers)-1):
        for j in range(i+1,len(numbers)):

Mynuddin-dev

import cv2
from imutils.video import WebcamVideoStream
from imutils.video import FPS
import imutils
import numpy as np
import time
import dlib
import cv2
import os
import math

Mynuddin-dev

with open("utt2spk") as f:
    liness = f.readlines()


utt2spk_final = open("utt2spk_final.txt" , 'w')
for line in liness:
    words = line.split()
    utt = (words[1],words[0])
    uttjoin = '-'.join(utt)
    # print(uttjoin)

Mynuddin-dev

with open("librispeech-lexicon.txt") as f:
    liness = f.readlines()

vocabulary = open("vocab.txt" , 'w')
for line in liness:
   vocabulary.write(line.split()[0] +"\n")

Mynuddin-dev

import cv2
import os
import numpy as np
path = "/home/myn/Downloads/all_images/"
destination = "/home/myn/Downloads/resized_all_images"
for img_name in os.listdir(path):
    # print(img_path)
    img = cv2.imread(str(path)+"/"+str(img_name))
    # print(img.shape)
    img = cv2.resize(img, (200,200), interpolation = cv2.INTER_AREA)

Mynuddin-dev

import cv2
import glob
import numpy as np
import os
import sys
import tensorflow as tf

from sklearn.model_selection import train_test_split

EPOCHS = 10

Mynuddin-dev

from keras.datasets import mnist
from keras.utils import to_categorical
from keras.layers import *
from keras.models import *
import numpy as np

## Load and preprocess the MNIST dataset
(train_images , train_labels) , (validation_images , validation_labels) = mnist.load_data()
## reshaping the images and normalizing the pixel values to be between 0 and 1.
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255

Mynuddin-dev

import glob
from importlib.resources import path
from os import listdir
from os.path import isfile, join
import cv2
import os
import numpy as np
from random import shuffle
# from tqdm import tqdm
import pandas as pd