Optimizing the Perceptron Output
Explore how the perceptron model predicts outputs, compares them with actual values using loss functions, and updates weights through gradient descent to find the optimal decision boundary. Understand the role of learning rate, error convergence, and epochs in training a simple neural network using NumPy.
The perceptron trick
To find the best possible boundary, the perceptron algorithm should predict the output, compare it with the actual output, and learn the optimal weights for predicting the best possible fit function that separates the two classes.
๐ ** Question: How does the model learn?**
The model learns for a couple of iterations until it finds the best possible boundary that separates the two classes. An initial-boundary is drawn and then the error is computed. In each iteration, the boundary line moves in the direction so that it minimizes the error. This process continues until the error is below a certain threshold.
The following illustration will help you visualize this:
Quiz
What did you observe in the illustration above? Are we moving the point closer to the line or the line closer to the misclassified point?
Line closer to the misclassified point
Misclassified point closer to the line
Predict the output
Recall the perceptron equation for making a boundary line:
= { 1 if >= 0 and 0 otherwise}
In case of a sigmoid function:
= { 1 if >= ...