ML Training

Training a perceptron in machine learning involves adjusting the weights of the perceptron based on the input data and corresponding outputs to minimize the error in predictions. A perceptron is a type of artificial neural network and a fundamental building block of many machine learning models.

Components of a Perceptron

1: Inputs (x1, x2, ..., xn):

• The features of the data that are fed into the perceptron.

2: Weights (w1, w2, ..., wn):

• Each input has an associated weight that determines its impact on the output.

3: Bias (b):

• A constant added to the weighted sum of the inputs to adjust the output.

4: Activation Function:

• A function (often a step function) that determines the output of the perceptron. For a binary classification task, the perceptron outputs 1 if the weighted sum of the inputs exceeds a threshold and 0 otherwise.

Training Process

The training of a perceptron typically involves the following steps:

1: Initialization:

• Initialize the weights and bias, usually with small random values.

2: Forward Pass:

• Calculate the weighted sum of the inputs and apply the activation function to produce an output.

3: Error Calculation:

• Compute the error, which is the difference between the predicted output and the actual output (label).

4: Weight Update:

• Adjust the weights and bias to minimize the error using a learning rule. The most common rule is the Perceptron Learning Rule:

5: Iteration:

• Repeat the forward pass, error calculation, and weight update steps for multiple iterations (epochs) until the error is minimized or another stopping criterion is met.

Example of Training a Perceptron

Let's illustrate the training process with a simple example of binary classification:

Dataset:

Assume we have a dataset with two features (x1, x2) and binary labels (0 or 1).

2: Initialization:

• Initialize weights W1 and W2 and bias b to small random values.

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