Equation in AI
In machine learning, the model often starts with a linear equation:
y = w_1x_1 + w_2x_2 + \dots + b
Inputs = features (e.g., number of rooms in a house, area in sq. ft, etc.)
Weights = importance given to each feature
Bias = baseline adjustment
Output = prediction (e.g., house price)
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2. How Weights Are Learned
Initially, weights are set randomly (like guessing).
The model makes a prediction.
It compares prediction vs. actual answer (this difference = error/loss).
Using an algorithm like gradient descent, the model adjusts weights step by step to reduce error.
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3. Simple Example: Predicting House Price
Equation:
Price = (w_1 \times \text{Area}) + (w_2 \times \text{Bedrooms}) + b
Suppose training data says:
A 1000 sq. ft, 2-bedroom house = $300k
A 2000 sq. ft, 3-bedroom house = $500k
The model might learn weights like:
(each sq. ft adds $150)
(each bedroom adds $20,000)
(no baseline adjustment)
So:
Price = 150 \times \text{Area} + 20{,}000 \times \text{Bedrooms}
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4. Intuition
If is large → Area matters a lot.
If is small → Bedrooms don’t influence much.
AI keeps tweaking weights until the predictions match reality closely.
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👉 In short:
Weights = knobs AI turns to “tune” importance of inputs.
Training = the process of finding the best knob settings.
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