Chapter 1 Introduction to Python Cut out only the good points of deep learning made from scratch

Python

Python is used in colleges, vocational schools, and computer science classes. It is also used by IT companies such as Google, MS, and FB. If you use machine learning, it's called Python.

Python version

There are 2 series and 3 series. This time I will write everything in 3 series.

External library

• Numpy
• Matplotlib

Python interpreter

Arithmetic calculation

>>> 1 - 2
-1
>>> 4 * 5
20
>>> 7 / 5
1.4
>>> 3 ** 2
9

Data type

>>> type(10)
int
>>> type(2.718)
float
>>> type('hello')
str

variable

>>> x = 10
>>> print(x)
10
>>> x = 100
>>> y = 3.14
>>> x * y
314.0

list

>>> a = [1,2,3,4,5]
>>> len(a)
5
>>> a[0]
1
>>> a[0:2]
[1,2]
>>> a[1:]
[2,3,4,5]
>>> a[:-1]
[1,2,3,4]

dictionary

>>> me = {'height': 100 }
>>> me['height']
100

Boolean

>>> hungry = True
>>> sleepy = False

if statement

>>> hungry = True
>>> if hungry:
...  print('I'm hungry')
...

for statement

>>> for i in [1,2,3]:

function

>>> def hello():

class

class name
    def __init__(self, xxx, xxx) # constructor
        ...

    def method1 # method1
        ...
    def method2 # method2
        ...

for expample

class Man
    def __init__(self, name)
        self.name = name
        print('init')

    def hello(self):
       print('hello' + self.name)

    def goodbye(self)
       print('goodby' + self.name)

Numpy

import

>>> import numpy as np

You can read numpy as np.

Array generation

>>> x = np.array([1.0,2.0,3.0])
>>> print(x)
[1.2.3.]
>>> type(x)
numpy.ndarray

Calculation

>>> x = np.array([1.0, 2.0, 3.0])
>>> y = np.array([2.0, 4.0, 6.0])
>>> x + y
array([3., 6., 9.])
>>> x- y
array([-1., -2., -3.])
>>> x * y
array([2., 8., 18.])
>>> x / y
array([0.5, 0.5, 0.5])

>>> x = np.array([1.0, 2.0, 3.0])
>>> x/2.0
array([0.5, 1., 1.5])

The number of elements of x and y must be the same

N-dimensional array

>>> A = np.array([[1,2], [2,3]])
>>> print(A)
[
 [1,2]
 [3,4]
]
>>> A.shape
(2,2)
>>> A.dtype
dtype('int64')

>>> B = np.array([3,0], [0,6])
>>> A+B
array([4,2], [3,10])
>>> A*B
array([3,0], [0,24])
>>> print(A)
[[1 2] [3 4]]
>>> A * 10
[[10 20][30 40]]

broadcast

A = np.array([1,2][3,4]) B = np.array([10, 20]) A*B array([10, 40][30, 80])

Access to elements

>>> X = np.array([51,55], [14,19], [0,4])
>>> print(X)
>>> X[0][1]
55

for row in X:
    print(row)

[51 55]
[14 19]
[0 4]

Conversion to a one-dimensional array

X = X.flattern()
[51 55 14 19 0 4]
>>> X[np.array([0, 2, 4])]
array([51, 14, 0])
>>> X > 15
array([T, T, F, T, F, F])
>>> X[X>15]
array([51,55,19])

Matplotlib

Drawing a simple graph

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(0, 6, 0.1)
y = np.sin(x)

plt.plot(x, y)
plt.show() ##Graph drawing

pyplot function

--Multiple graphs can be displayed --Images can also be displayed