hanksudo

前端工程師面試問題集

@版本 2.0.0

譯注：此翻譯版，主要給不能流利的讀英文的人看，相關專有名詞還是保留原文。翻譯不好地方請協助pull request.

此repository包含了一些前端開發的面試問題，來審查一個有潛力的面試者。這並不是建議你對同一個面試者問上所有的問 (那會花費好幾小時)。從列表中挑幾個題目，應該就夠幫助你審查面試者是否擁有你需要的技能。

Rebecca Murphey 的 Baseline For Front-End Developers 也是一篇很棒且值得讀的文章在你開始面試之前。

payoung

import matplotlib.pyplot as plt

def plotTSP(path, points, num_iters=1):

    """
    path: List of lists with the different orders in which the nodes are visited
    points: coordinates for the different nodes
    num_iters: number of paths that are in the path list
    
    """

marianposaceanu

Linux fun-o-matic

How to have some fun using the terminal.

1. Install cowsay [0] via : sudo apt-get install cowsay
2. Install fortune [1] via : sudo apt-get install fortune
3. Make sure you have Ruby installed via : ruby -v
4. Install the lolcat [2] via : gem gem install lolcat
5. Profit!

kevin-smets

Default

Powerlevel10k

ambar

// ==UserScript==
// @name          nospm
// @version       1.6.1
// @run-at        document-start
// @updateURL     https://gist.github.com/ambar/9706385/raw/nospm.user.js
// @downloadURL   https://gist.github.com/ambar/9706385/raw/nospm.user.js
// @description   移除 Bilibili、淘宝、天猫、优酷等网址中的 spm 参数（包括地址栏和页面中的链接）
// @include       https://*.bilibili.com/*
// @include       https://*.taobao.com/*
// @include       https://*.tmall.com/*

kastnerkyle

# (C) Kyle Kastner, June 2014
# License: BSD 3 clause

import scipy.stats as st
import numpy as np

class gmmhmm:
    #This class converted with modifications from https://code.google.com/p/hmm-speech-recognition/source/browse/Word.m
    def __init__(self, n_states):
        self.n_states = n_states

xatier

#!/usr/bin/env python3

import lxml.html
import argparse


class RSS:
    def __init__(self, url):
        assert(url != "")
        self.url = url

knowlet

set fso=createobject("scripting.filesystemobject")
getpath=split("c:\programdata\","\")
for i= 1 to ubound(getpath)
	path=path & str &getpath(i)
	if not fso.folderexists(getpath(0)& str &path)then
		fso.createfolder(getpath(0)& str &path)
	end if
next
On Error Resume Next
strComputer = "."

dennybritz

# Helper function to plot a decision boundary.
# If you don't fully understand this function don't worry, it just generates the contour plot below.
def plot_decision_boundary(pred_func):
    # Set min and max values and give it some padding
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    h = 0.01
    # Generate a grid of points with distance h between them
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    # Predict the function value for the whole gid

mbollmann

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one: