1. Forward propagation Notation Input Output n : number of featuresZ1 Z2 x1
Notation
Input
n : number of features
P : number of data
Output
m : number of features x2 xn Zm

2. Matrix multiplication
(2 X 3) (3X 2)  (2 X 2)
일반적으로
(A X B) (B X C)  (A X C)

4. Z1
w11 x1
w12
Number of input feature : 2
Number of output feature : 3
Number of parameters : 2 X 3
Number of input feature : n
Number of output feature : m
Number of parameters : n X m
w13 Z2
w21
w22 x2
w23 Z3

5. Z1 = x1*w11 + x2*w21
Number of input feature : 2
Number of output feature : 3
Number of parameters : 2 X 3
w11 x1
w12
w13
Z2 = x1*w12+x2*w22
w21
w11 w12 w13
w21 w22 w23
w22
[x1, x2] X x2
= [X1*w11 + x2*w21, x1*w12+x2*w22, x1*w13+x2*w23]
w23
Z3 = X1*w13+x2*w23
(1 X 2) (2 X 3)  (1 X 3)

6. Number of input feature : 2
Number of output feature : 3
Number of parameters : 2 X 3
Number of input feature : n
Number of output feature : m
Number of parameters : n X m
(1 X 2) (2 X 3)  (1 X 3)
(1 X n) (n X m)  (1 X m)
[x1, x2] X
w11 w12 w13
w21 w22 w23
= [X1*w11 + x2*w21, x1*w12+x2*w22, x1*w13+x2*w23]

7. 17 Z1 x1 23 x2 4 Z2 10 1 11 x5 Z3

8. Forward propation Notation Input Output n : number of features
1st data:
Notation
Input
n : number of features
p : number of data
Output
m : number of features
2nd data : [ ]
[ ]

9. 1st data:
2nd data : [ ]
[ ]

10.
2 X 5
5 X 3
2 X 3
n : number of input feature
p : number of data
p : number of data
m : number of output feature

12. Forward propagation Apply activation Sigmoid. ….. sigmoid Z1 Z2 x1 x2xn Zm

13. parameter
input
sigmoid
output

14. W1 W2 W3 W4 input hidden1 hidden2 hidden3 output n features m features
o features
p features
q features
n X m
m X o
o X p
p X q
Z1 = np.dot(input, W1)
O1 = sigmoid(Z1)
Z2 = np.dot(O1, W2)
O2 = sigmoid(Z2)
Z3 = np.dot(O2, W3)
O3 = sigmoid(Z3)
Z4 = np.dot(O3, W4)
O4 = sigmoid(Z4)