Lawrence Chen lawrencechen0921

A curated list of AWS resources to prepare for the AWS Certifications

A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.

	import tensorflow as tf
	import keras
	(x_train, y_train), (x_test, y_test) = keras.datasets.boston_housing.load_data()

	%matplotlib inline

	import matplotlib.pyplot as plt

	plt.rcParams['font size'] = 10*3
	plt.rcParams['figure.figsize'] = [18, 12]

	public class QuickFindUF
	{
	private int [ ] id;

	public QuickFindUF( int N)
	{
	id = new int[N]; # set id of each object to itself { N array accesses}
	for ( int i = 0 ; i < N ; i ++)
	id[ i ] = i ;
	}

	public static void main(String[ ] args)
	{
	int N = StdIn. readInt( );
	UF uf = new UF(N) ;
	while (! StdIn . isEmpty( ) )
	{
	int p = StdIn . readInt( );
	int q = StdIn . readInt( );
	if (! uf . connected(p, q) )
	{

	function factorial (num) {
	if (num === 1) {
	return num
	} else {
	return num * factorial(num - 1)
	}
	}
	factorail(5)

	#LIFO 觀念

	from __future__ import absolute_import, division, print_function, unicode_literals
	import tensorflow as tf

	mnist = tf.keras.datasets.mnist

	(x_train, y_train), (x_test, y_test) = mnist.load_data()
	x_train, x_test = x_train / 255.0, x_test / 255.0

	import tensorflow as tf
	a = tf.constant(1, name='a')
	b = tf.constant(1, name='b')
	c = a+b
	with tf.Session as sess: #注意以上的session將a,b 視為個體, 否則跑出來的結果會為不同
	print(sess.run(c))


	import tensorflow as tf
	a = tf.constant(1, name='a')

	from bs4 import BeautifulSoup as soup
	fin=open('web.htm', encoding='utf-8') #將html檔案放置 web.htm(再轉換到fin開啟)
	s=fin.read()
	htm=soup(s,'html.parser')
	print(htm.title.prettify())
	print(htm.title.contents)
	print(htm.contents[0])
	print(htm.title.name)
	print(htm.title.string)
	print(htm.meta)

	from FinMind.Data import Load
	import numpy as np
	#import pandas as pd
	from keras.layers.core import Dense, Dropout
	from keras.layers import LSTM , GRU
	from keras.models import Sequential
	from sklearn.preprocessing import MinMaxScaler
	import matplotlib.pyplot as plt
	import math
	from keras.optimizers import Adam