文章出处，原创于 https://HawkingOuYang.github.io/

机器学习：

torch

theano

Caffe

mxnet

苹果官方：https://developer.apple.com/machine-learning/
Neural Network: http://playground.tensorflow.org

图片识别：http://clarifai.com/demo
语音识别paper：https://blog.acolyer.org/2016/04/19/
语音识别应用：https://v.qq.com/x/page/q035357zsxx.html
机器翻译（google）
图片生成：https://arxiv.org/pdf/1605.05396v2.pdf
图片生成应用，阿里“鲁班”

来自硅谷的终身学习平台：https://www.bittiger.io/ http://community.bittiger.io/
数据科学家之路：https://lenadroid.github.io/posts/machine-learning-fsharp-accorddotnet.html

Python的编程利器：http://jupyter.org/install.html
在浏览器写Python：https://try.jupyter.org/
pip install wheel
pip install panda
pip install matplotlib
pip install scipy

import pandas as pd
import matplotlib.pyplot as plt # plotting
import numpy as np # dense matrices
from scipy.sparse import csr_matrix # sparse matrices
%matplotlib inline

df = pd.read_csv(‘1111.csv’)

爬虫
https://movie.douban.com/subject/26363254/
pip install -U kaggle-cli
pip install –upgrade html5lib==1.0b8
pip install beautifulsoup4
pip install request

from bs4 import BeautifulSoup
import requests

response = requests.get(‘https://movie.douban.com/subject/26363254/‘)

bs = BeautifulSoup(response.text, ‘lxml’)

name = bs.find(‘span’, attrs={‘property’:’v:itemreviewed’}).text
score = bs.find(‘strong’, attrs={‘property’:’v:average’}).text
sumary = bs.find(‘div’, id=’link-report’).text.strip()

print(name, score, sumary)

机器学习 - 线性回归
pip install sklearn

import numpy as np # dense matrices
import matplotlib.pyplot as plt # plotting
%matplotlib inline

x = np.linspace(1,10,20)
a = np.random.randint(1,10,20)

y = x * 4 + a

x,a,y

plt.plot(x,y,’o’)

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(x.reshape(20,1), y.reshape(20,1))

model.predict(6)

model.coef_

model.intercept_

y = kx + b

3.82222222 * 6 + 5.87777778