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Simple linear model example

y = w1x1 + w2x2 + b

Here w1, w2, and b are parameters (2 weights + 1 bias = 3 parameters total)

class SimpleLinearModel:
def init(self, input_features=2):

Initialize parameters randomly

self.weights = [0.5, -0.3]

w1, w2

self.bias = 0.1

b

self.total_parameters = len(self.weights) + 1

def predict(self, inputs):

Calculate prediction using parameters

    prediction = self.bias
    for i, weight in enumerate(self.weights):
        prediction += weight * inputs[i]
    return prediction

def update_parameters(self, learning_rate, gradients):

During training, parameters get updated

    for i in range(len(self.weights)):
        self.weights[i] -= learning_rate * gradients['weights'][i]
    self.bias -= learning_rate * gradients['bias']

Example usage

model = SimpleLinearModel()
print(f"Model has {model.total_parameters} parameters")

Input: [height=170cm, age=25]

prediction = model.predict([170, 25])
print(f"Prediction: {prediction}")

### Neural Network Parameters

In neural networks, parameters are primarily weights and biases that connect neurons across layers:

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