IT討論區(57) 大佬呀又唔開post
恩地BABY
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IT狗思考方法
一係BA/PM
一係 network
一係做看更
一係 network
一係做看更
做看更啦
反正做IT到中年都係做看更
反正做IT到中年都係做看更
打坐好工
打code 打多幾年都係做看更
不如早啲做看更拎經驗
之後升做高級看更搵多啲
不如早啲做看更拎經驗
之後升做高級看更搵多啲
IFC樓下個D看更要識好多language
係咪尖吵咀個間
召喚強手
假設有10000句phase, eg "coffee workshop", "cat owner meeting" 要搵相似既phase做cluster
Step 1: English normalisation, 將meeting, met 變做meet. ..
Step2: for each unique normalized word, get a dictionary of occurrence. "meet":6％, "coffee" 0.5％, workshop 5%....
Step3: build a co-exist-probability matrix for the phases.
"Cat ownership talk" vs "cat owner meeting" = prob of "cat" * prob of "own"
..........
Step4: build a graph, each phase is a node, if the co-exist-probability of two node is smaller than x％， build an edge between the two node.
Step 5: use graph algorithm to find all cluster.
會唔會做多左，定係已經有library做緊呢D
假設有10000句phase, eg "coffee workshop", "cat owner meeting" 要搵相似既phase做cluster
Step 1: English normalisation, 將meeting, met 變做meet. ..
Step2: for each unique normalized word, get a dictionary of occurrence. "meet":6％, "coffee" 0.5％, workshop 5%....
Step3: build a co-exist-probability matrix for the phases.
"Cat ownership talk" vs "cat owner meeting" = prob of "cat" * prob of "own"
..........
Step4: build a graph, each phase is a node, if the co-exist-probability of two node is smaller than x％， build an edge between the two node.
Step 5: use graph algorithm to find all cluster.
會唔會做多左，定係已經有library做緊呢D
可能已經有人做左🤔你入返你d training data 就okay
搵下有冇open source
搵下有冇open source
我堆text 好多specific noun , 驚用library太loose
有冇人知build 個matrix 係咪一定要 o(n^2）
有冇人知build 個matrix 係咪一定要 o(n^2）
睇你能力
想做Quant Dev搵真銀
求教路
求教路
buzz word, 入假quant扮真quant
你同手黏好似就係Quant Dev / Data Engine