| # Enable Powerlevel10k instant prompt. Should stay close to the top of ~/.zshrc. | |
| # Initialization code that may require console input (password prompts, [y/n] | |
| # confirmations, etc.) must go above this block; everything else may go below. | |
| if [[ -r "${XDG_CACHE_HOME:-$HOME/.cache}/p10k-instant-prompt-${(%):-%n}.zsh" ]]; then | |
| source "${XDG_CACHE_HOME:-$HOME/.cache}/p10k-instant-prompt-${(%):-%n}.zsh" | |
| fi | |
| # >>> conda initialize >>> | |
| # !! Contents within this block are managed by 'conda init' !! |
| from selenium import webdriver | |
| from selenium.webdriver.common.keys import Keys | |
| from selenium.webdriver.common.action_chains import ActionChains | |
| import time | |
| driver = webdriver.Chrome("drivers/chromedriver") | |
| try: | |
| driver.get("http://www.google.com") | |
| input_element = driver.find_element_by_name("q") |
| # encoding: utf-8 | |
| """ | |
| @author: BrikerMan | |
| @contact: eliyar917@gmail.com | |
| @blog: https://eliyar.biz | |
| @version: 1.0 | |
| @license: Apache Licence | |
| @file: w2v_visualizer.py | |
| @time: 2017/7/30 上午9:37 | |
| """ |
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.
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| # For the maths used in Convonets please refer to the Andrej Karpathy blog and this link > http://cs231n.github.io/convolutional-networks/#conv | |
| # IF you want to go in detail please read this -> https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf | |
| import argparse | |
| import sys | |
| #importing datset from tensorflow | |
| from tensorflow.examples.tutorials.mnist import input_data | |
| #importing tensorflow for computation | |
| import tensorflow as tf |
| import numpy as np | |
| epochs = 3 #Change and see the results | |
| # Layers | |
| inputLayerSize, hiddenLayerSize, outputLayerSize = 2,3,1 | |
| #Learning Rate | |
| L = 0.1 | |
| #input | |
| X = np.array([[0,0], [0,1], [1,0], [1,1]]) | |
| #Output |
