Machine Learning 简明教程

import numpy as np

pip install numpy

import numpy as np
data = np.array([1,2,3,4,5])
print(data)
print(len(data))
print(type(data))
print(data.shape)

[1 2 3 4 5]
5
<class 'numpy.ndarray'>
(5,)

import pandas as pd

pip install pandas

import pandas as pd
import numpy as np
data = np.array(['g','a','u','r','a','v'])
s = pd.Series(data)
print (s)

0    g
1    a
2    u
3    r
4    a
5    v
dtype: object

from scipy import linalg

pip install scipy

import numpy as np
import scipy
from scipy import linalg
A= np.array([[1,2],[3,4]])
print(linalg.inv(A))

[[-2.   1. ]
 [ 1.5 -0.5]]

from sklearn.datasets import load_breast_cancer

pip install scikit-learn

from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
print(data.target[[10, 50, 85]])
print(list(data.target_names))

[0 1 0]
['malignant', 'benign']

pip3 install torch torchvision torchaudio

import torch

import numpy as np
import torch
x = np.ones([3,4])
y = torch.from_numpy(x)
print(y)

tensor([[1., 1., 1., 1.],
        [1., 1., 1., 1.],
        [1., 1., 1., 1.]], dtype=torch.float64)

pip install tensorflow

import tensorflow as tf

import tensorflow as tf
data = tf.constant([[2,1],[4,6]])
print(data)

tf.Tensor(
[[2 1]
 [4 6]], shape=(2, 2), dtype=int32)

pip install keras

import keras

import keras
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
print(x_train.shape)
print(x_test.shape)
print(y_train.shape)
print(y_test.shape)

(50000, 32, 32, 3)
(10000, 32, 32, 3)
(50000, 1)
(10000, 1)

pip install matplotlib

import matplotlib.pyplot as plt

import matplotlib.pyplot as plt
plt.plot([1,2,3],[1,2,3])
plt.show()

pip install seaborn

import seaborn as sns