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-- Sep 20 In-Class Exercise

(Edited: 2017-09-20)

((resource:WhatsApp Image 2017-09-20 at 5.36.14 PM.jpeg|Resource Description for WhatsApp Image 2017-09-20 at 5.36.14 PM.jpeg))

-- Sep 20 In-Class Exercise

We can separate the 1's and 0's using planes like so:

We can separate the 1's and 0's using planes like so: ((resource:Inclass exercise.png|Resource Description for Inclass exercise.png))

-- Sep 20 In-Class Exercise

((resource:WhatsApp Image 2017-09-20 at 5.47.14 PM.jpeg|Resource Description for WhatsApp Image 2017-09-20 at 5.47.14 PM.jpeg))

-- Sep 20 In-Class Exercise

a few planes that are supposed to separate the point where the output is "1" one plane can separate the top point (1,1,1)

 two planes at the bottom to separate the other three points (0,0,1) (0,1,0) (1,0,0)

a fourth one to connect the network. input: x,y,z P1, P2, P3 will feed to P4

a few planes that are supposed to separate the point where the output is "1" one plane can separate the top point (1,1,1) two planes at the bottom to separate the other three points (0,0,1) (0,1,0) (1,0,0) a fourth one to connect the network. input: x,y,z P1, P2, P3 will feed to P4

-- Sep 20 In-Class Exercise

((resource:IMG_3238.JPG|Resource Description for IMG_3238.JPG))

-- Sep 20 In-Class Exercise

permutations : 0 0 0 -> 0

0 0 1 -> 1

0 1 0 -> 1

0 1 1 -> 0

1 0 0 -> 1

1 0 1 -> 0

1 1 0 -> 0

1 1 1 -> 1

Theta 1 : 0.1 W = [1 1 1]

Theta 2 : 1 [0.5 0.5 0.5]

Theta 3: 2.5 W = [1 1 1]

Resource Description for IMG_20170920_223059576.jpg

(Edited: 2017-09-20)

permutations : 0 0 0 -> 0 0 0 1 -> 1 0 1 0 -> 1 0 1 1 -> 0 1 0 0 -> 1 1 0 1 -> 0 1 1 0 -> 0 1 1 1 -> 1 Theta 1 : 0.1 W = [1 1 1] Theta 2 : 1 [0.5 0.5 0.5] Theta 3: 2.5 W = [1 1 1] ((resource:IMG_20170920_223059576.jpg|Resource Description for IMG_20170920_223059576.jpg))

-- Sep 20 In-Class Exercise

((resource:1505974338617136600510.jpg|Resource Description for 1505974338617136600510.jpg))

-- Sep 20 In-Class Exercise

((resource:IMG_20170920_234807.jpg|Resource Description for IMG_20170920_234807.jpg))

-- Sep 20 In-Class Exercise

((resource:in_class_sep20.jpg|Resource Description for in_class_sep20.jpg))

-- Sep 20 In-Class Exercise

For a two-layer perceptron network, it has been shown that an N-bit Boolean parity function is implemented with N+1 perceptrons.

So , for PAR(x1, x2, x3), it will be # perceptrons = 4.

Perceptrons:

Hidden Layer:

[1 1 1] -> 1

[0 0 1] -> 1

[1 0 0] -> 1

Output Layer:

[1 1 1] -> 1

N ->

N -> N

N ->

For a two-layer perceptron network, it has been shown that an N-bit Boolean parity function is implemented with N+1 perceptrons. So , for PAR(x1, x2, x3), it will be # perceptrons = 4. Perceptrons: Hidden Layer: [1 1 1] -> 1 [0 0 1] -> 1 [1 0 0] -> 1 Output Layer: [1 1 1] -> 1 N -> N -> N N ->