antondevv

<template>
  <div class="mother">
    <div class="background"></div>

    <img
      v-if="windowWidth >= 1024"
      class="desktop-image"
      :src="require('@/assets/img/e-book-topimage.png')"
      alt=""
    />

antondevv

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

#Print our data
data = pd.read_csv('data.csv')
print(data)

plt.scatter(data['weight'], data['height'])
plt.xlabel("Weight")

antondevv

weight,height
1,3
2,5
4,6

antondevv

def cost_function(m, b, points):
    the_total_error = 0
    sum_error = 0
    for i in range(len(points)):
        x = points.iloc[i].weight
        y = points.iloc[i].height
        the_total_error += (y - (m * x + b)) ** 2
        sum_error += the_total_error
    print(sum_error)
    return sum_error

antondevv

m = 0
b = 0
Learning_rate = 0.00000005
cost = 0
times_to_iterate = 1000

antondevv

def gradient_descent(m_now, b_now, points, L):
    the_slope_of_thecost_depending_on_m = 0
    the_slope_of_thecost_depending_on_b = 0

    n = len(points)

    for i in range(n):
        x = points.iloc[i].weight
        y = points.iloc[i].height
        the_slope_of_thecost_depending_on_m += - (2/n) * x * (y - (m_now * x + b_now))

antondevv

for i in range(times_to_iterate):
    m, b = gradient_descent(m, b, data, Learning_rate)
    
plt.scatter(data['weight'], data['height'])
plt.plot(list(range(0, 5)), [m * x + b for x in range(0 , 5)], color="red")
plt.show()

antondevv

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

data = pd.read_csv('data.csv')
# print(data)

def cost_function(m, b, points):
    the_total_error = 0
    sum_error = 0

antondevv

What a beautiful day to be outside!

antondevv

text = 'What a beautiful day to be outside, incredibly beautiful day!'
text = text.lower()
tokenized = text.split()