【第三課】kaggle案例分析三

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比賽題目介紹

• TalkingData是中國最大的第三方移動數據平台，移動設備用戶日常的選擇和行為用戶畫像。目前，TalkingData正在尋求每天在中國活躍的5億移動設備70%以上的行為數據，幫助客戶更好的了解與其用戶互動。
• TalkingData提供了大約20萬用戶的數據(全部真實且經過脫敏處理)，按照年齡和性別分成12個組，比如男性22到25歲，女性30到35歲，同時提供了用戶行為屬性，比如在什么樣的時間點出現什么樣的地理位置等等，選手通過這些信息去推測用戶是分在哪一個性別年齡里面。
• 什么是用戶畫像
• 用戶畫像可以理解為就是為用戶畫標簽，標簽是不固定的，依照企業業務場景動態添加、刪除等，有的標簽是永久的，有的標簽是可以動態變更的
• 用戶畫像可以看做是綜合性的標簽系統，在自身擁有(或從第三方獲得)的原始數據中，從多個維度對用戶進行產品進行刻畫，提取出商業價值潛力的語義信息，是常見的企業級大數據應用
• 對於人常見標簽與維度，地域、年齡、性別、文化、職業、收入、生活習慣、消費習慣等
• 對於產品常見刻畫維度，產品類別、活躍頻率、產品喜好、產品驅動、使用習慣、產品消費等
• 技術棧：無監督學習、半監督學習(技巧性很強)，大多數是有監督學習
• 用戶畫像的作用
• 精准營銷，分析產品潛在客戶，針對特定群體利用短信郵件等方式進行營銷
• 用戶統計，比如中國大學購買書籍人數TOP10，全國分城市指數等
• 數據挖掘，構建智能推薦系統，利用關聯規則計算，喜歡紅酒的通常喜歡什么運動品牌，利用聚類算法分析，喜歡紅酒的人的年齡段分布情況
• 進行效果評估，完善產品運用，提升服務質量，其實這也就是相當於市場調研、用戶調研，迅速下定位服務群體，提供高水平的服務
• 對服務或產品進行私人定制，即個性化的服務某類群體甚至每一位用戶(這是未來的消費趨勢)比如，某公司退出一款5-10歲兒童的玩具，通過用戶畫像分析，發現某些特征的比重最大，就給產品提供了非常可觀的決策依據
• 業務經營分析以及競爭分析，影響企業發展戰略

人工神經網絡原理

• 知識地圖
• 從單層感知器到多層感知器
• 從多層感知器到自編碼器
• 從多層感知器到卷積神經網絡，再到深度殘差網絡
• 從多層感知器到遞歸神經網絡，再到LSTM
• 從單層感知器到Hopfield神經網絡，再到Bolazmann機和RBM
• 用RBM堆疊成DBN，DBN與多層自編碼器結合成DBN-DNN
• 神經網絡要素
• 網路結構(全連接、分層、有時滯回路、權值共享、激活函數)
• 運行機制(異步更新、同步更新、前饋)
• 訓練算法，訓練中使用的trick(mini batch BN drop out等)，損失函數的定義
• 訓練數據(數據的預處理，輸入和輸出的構成等)
• 單層感知器
• 輸入節點
• 輸出節點
• 權向量
• 偏置因子
• 激活函數
• 學習率
• 單層感知器學習算法
• 單層感知器的局限
  激活函數

sng(w1​x1​+w2​x2​+w3x3​)=0

單層感知器類比於線性分類器

感知器學習的規則

1958年，首先定義了一個具有單層計算單元的神經網絡結構，稱為感知器
感知器的學習規則規定，學習信號等於神經元期望輸出(教師信號)與實際輸出之差

r=dj​−oj​

式中，dj​為期望的輸出，oj​=f(WjT​X)感知器采用了與閾值轉移函數類似的符號轉移函數，其表達式為

f(WjT​X)=sgn(WjT​X)={1,−1,​(WjT​≥0)(WjT​<0)​

因此，權值調整公式為：

ΔWj​=±2ηX

感知器學習規則只適用於二進制神經元，初始值可取任意值。
感知器學習規則代表一種有導師學習，由於感知器理論是研究其他神經網絡的基礎，該規則對於神經網絡的有導師學習具有極為重要的意義。

多層前饋神經網絡（BP網絡）

• 隱藏層與隱藏節點
• 前饋-----每一層的節點僅和下一層節點相連

BP學習算法本質就是梯度下降法

神經網絡的兩個過程：1.訓練過程 2.推斷過程

小結：數學背景：梯度下降法(使用誤差平方和構造誤差函數)
梯度下降法給出權值，通過反向傳播誤差逐層訓練，推斷時誤差往后走，構造完后給出權值，誤差信號通過公式計算梯度，得出梯度對權值進行迭代改變，知道權值不再變化或者次數足夠收斂，就結束整個訓練過程

在python深度學習中，可以使用keras進行簡單的神經網絡等設計
keras框架是簡單方便的，模塊可以作為數據或序列的運算圖等，keras中的各個設計環節都是單獨的一個模塊，所以需要根據自己的需求來設計
keras中文文檔
keras默認使用tensorflow，如果使用theano需要修改文件中的路徑內的json文件

keras的基本使用

• Sequential 模型構建
• 序貫模型是多個網絡層的線性堆疊
• 可以通過向Sequential模型傳遞一個layer的list來構造該模型

from keras.models import Sequential
from keras.layers import Dense,Activation
model = Sequential([    
Dense(32,units=784),    
Activation('relu'),    
Dense(10),    
Activation('softmax'),])

• 也可以通過.add()方法一個個的將layer加入模型中

model = Sequential()
model.add(Dense(32,input_shape=(784,))
)model.add(Activation('relu'))

• 指定輸入數據的shape
• input shape（元祖型數據）
• input dim

model = Sequential()
model.add(Dense(32,input_dim=784))

• input length

model = Sequentail()
model.add(Dense(32,input_shape=784))

• batch_size

評分標准

• 選手算出用戶在不同分組上的概率，現實中一個用戶只能在一個分組。理想狀態下如果能算出這個概率是1，其他是0的話，這個答案就是沒有任何損失的。一般來說，提交的答案中，某個用戶會有或大或小的概率屬於多個組別，這個時候就有概率上的損失，這個損失的高低代表答案的水平。我們優化函數或者說是評估指標就是下面的損失函數

logloss=−N1​i=1∑N​j=1∑M​yij​log(pij​)

max(min(p,1−10−15),10−15)

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