MACHINE LEARNING
Lecture 6
神經網路(Neural Network)
數據處理
模型
優化
訓練
Neural Network
Forwardpropagation
out
h1
b1
h2
w2
out = h_{1} \times w_{1} + h_{2} \times w_{2}+b_{1}
\frac{1}{1+e^{-out}}
Backpropagation
用前向傳播的資訊計算
再用連鎖律(對 又是他)
把權重的偏微分算出來更新
ㄜ一些噁心的東東
\frac{\partial Loss}{\partial w} =\frac{\partial Loss}{\partial h}\frac{\partial h}{\partial w}
我又來荼毒了對不幾
Loss = \frac{1}{2} (out - y)^{2}
今天的損失函數 ouo
in1
out
h1
w2
w1
\frac{\partial Loss}{\partial w_{1}} =in_{1}(out -y)w_{2}
\frac{\partial Loss}{\partial w2} =(out -y)h_{1}
ㄜ如果要用激發函數就要再用一次連鎖
bias ㄋ
\frac{\partial Loss}{\partial h}
\frac{\partial Loss}{\partial b} = (out-y) w_{2}
Training Set
and
Test Set
1+1=2 (80%)
1+2=? (20%)
Numpy Axis
二維
[[0, 4, 2], [-2, 5, 3]]
三維
[ [ [1, 2],[3, 4]], [ [5, 6],[7, 8] ],[[9, 10], [11, 12] ]]
Min-Max Normalization
X_{norm} = \frac{X - X_{min}}{X_{max}-X_{min}} \in [0,1]
X_{norm} = \frac{X - \mu}{X_{max}-X_{min}} \in [-1,1]
食酢
還記得雷夢要買房嗎(?
波士頓房價問題(?