Training a Model

Training involves forward pass, loss calculation, and backpropagation to adjust weights.

Syntax

AI

1
2
3
4

model.fit(X_train, y_train,
    epochs=10,
    batch_size=32,
    validation_split=0.2)

Training Loop Concept

PYTHON

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

# Training loop concept
def train(model, data, epochs, lr):
    for epoch in range(epochs):
        total_loss = 0
        for x, y_true in data:
            y_pred = model(x)
            loss = (y_pred - y_true) ** 2
            total_loss += loss

            # Update weights (simplified)
            gradient = 2 * (y_pred - y_true)
            model["w"] -= lr * gradient * x
            model["b"] -= lr * gradient

        if epoch % 100 == 0:
            print(f"Epoch {epoch}: Loss = {total_loss:.4f}")

model = {"w": 0.5, "b": 0.1}
data = [(1, 2), (2, 4), (3, 6), (4, 8)]
train(model, data, epochs=500, lr=0.01)
print(f"Learned: w={model[\"w\"]:.2f}, b={model[\"b\"]:.2f}")

Try It Yourself

Practice

1

Exercise

Practice this concept.

Answer

Write the code as shown above.

Quick Quiz

1

What did you learn?

Concept A

Concept B

Concept C

Concept D

This covers the basics.

Retry Quiz

Interview Questions

Explain this concept.

It is a fundamental AI feature.

Previous

What are Neural Networks?

Next Lesson

Activation Functions