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3. Output Layer: The output layer is the final layer of the Neural Network. It takes the processed information
from the hidden layers and provides the final output, which could be a prediction, classification, or any other
desired result based on the problem being solved.
Real-world applications of Neural Networks are fraud detection, recommendation system, facial recognition,
chatbots and virtual assistant, vegetable price prediction, etc.
How does AI make a Decision?
A perceptron is a basic unit of an Artificial Neural Network that mimics a biological neuron. It takes multiple
inputs, applies weights, sums them, and passes the result through an activation function to produce an output.
It serves as a fundamental building block of neural networks, enabling AI to classify and distinguish between
different inputs based on learned patterns.
For example, let's say you want to go for a picnic to the park today. What would be your thought process? What
would you consider?
On a sunny day, many of us would love to play and enjoy good food in the park. These factors can influence your
decision about whether to go for a picnic or not. For example, you might ask yourself:
• “Should I bring a cricket kit?” • “Should I bring a Tambola game to play?”
• "Will there be a big playground to play Cricket?" • “Will there be food outlets nearby?”
These questions help you plan your picnic and make the most of your day out.
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Should I bring a cricket kit? Should I bring a Will there be a big playground Will there be food outlets nearby?
Tambola game to play? to play Cricket?
Now, you have the factors that will guide your decision on what to do during the picnic. But take note, not all
factors are equal. Some factors are more important, while some are not. Let's see which one is more important.
Let us rank them from the most important to the least important. For me, “Will there be big playground to play
cricket?” is more important than “Will there be food outlets nearby?”. And “Should I bring a cricket kit?” is more
important than “Should I bring a Tambola game to play?”. We can put the ranking for this example.
Now let us convert this to perceptron.
In the above example, we have four factors as four inputs. So, let’s draw the perceptron with four inputs (from X1
to X4). Next, we have their weights (from W1 to W4). Then, we also have the bias B, with weight W . Then, we sum
B
all up as weighted sum (Σ). Then, the weighted sum is passed through an activation function (∫). The activation
function is a rule that helps a neuron decide whether to pass its signal forward, allowing the model to handle
complex problems. Finaly, the output is generated.
130 Artificial Intelligence Play (Ver 1.0)-X

