Page 133 - Ai_C10_Flipbook
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Inputs Weights
x W
1 1
Weighted Activation
x W
2 2 Sum Function Output
∑ Y
x W
3 3
W B B
x W
4 4
Perceptron Model
Now we will fill in the four inputs with the factors that we have listed in the sequence of our priority.
• Should I bring a cricket kit? • Should I bring a Tambola game to play?
• Will there be big playground to play Cricket? • Will there be food outlets nearby?
Perceptron: Go for Picnic
Should I bring a cricket kit?
1st
Should I bring a Tambola game Weighted Activation
to play? 2nd Sum Function Output
3rd ∑ Y
Will there be big playground to
play Cricket?
4th
W
B
B
Will there be food outlets nearby?
Now, we will assign weights to the factors. The values assigned to weights can be based on experience or personal
preference. For instance, someone who has experienced the fun of playing in a big playground at a picnic spot
might assign greater importance to having a big ground, believing it allows more people to join and increases
the enjoyment. On the other hand, personal preferences vary—someone who isn’t interested in physical activities
might give less importance to a large ground.
Similarly, the value assigned to W (weight for the big ground) can reflect personal choices or biases, which may
B
outweigh the influence of all other factors. A person who enjoys physical sports might set a lower value for W ,
B
ensuring the decision leans towards playing Cricket unless they are certain the ground is small. Conversely, someone
less inclined towards physical activities might assign a higher value to W , prioritising a game like Tambola regardless
B
of the size of the picnic spot. In the example below, we set W to 4 to reflect a cautious approach in decision-making.
B
Advanced Concepts of Modeling in AI 131

