Deep Learning
Basic
- 神经网络：
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监督学习：1个x对应1个y；
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Sigmoid:
s i g m o i d = 1 1 + e − x sigmoid=\frac{1}{1+e^{-x}} sigmoid=1+e−x1 -
ReLU :线性整流函数；
Logistic Regression
–>binary classification / x–>y 0 1
some sign
( x , y ) , x ∈ R n x , y ∈ 0 , 1 M = m t r a i n m t e s t = t e s t M : ( x ( 1 ) , y ( 1 ) ) , ( x ( 2 ) , y ( 2 ) ) . . . , ( x ( m ) , y ( m ) ) X = [ x ( 1 ) x ( 2 ) ⋯ x ( m ) ] ← n x × m (3) (x,y) , x\in{R^{n_{x}}},y\in{0,1}\\\\ M=m_{train}\quad m_{test}=test\\\\ M:{(x^{(1)},y^{(1)}),(x^{(2)},y^{(2)})...,(x^{(m)},y^{(m)})}\\\\ X = \left[ \begin{matrix} x^{(1)} & x^{(2)} &\cdots & x^{(m)} \end{matrix} \right] \tag{3}\leftarrow n^{x}\times m (x,y),x∈Rnx,y∈0,1M=mtrainmtest=testM:(x(1),y(1)),(x(2),y(2))...,(x(m),y(m))X=[x(1)x(2)⋯x(m)]←nx×m(3)
