# Generative AI Certificate Q&A: How does artificial neural network learn?

## Question

How does an artificial neural network learn?

A. A computer scientist programs each neuron to have the correct answer to any question.
B. Only correct answers go into the input layer, so it learns what’s correct from the output layer.
C. The hidden layers hide the incorrect answers from the rest of the network.
D. It looks at the data and makes guesses, then it compares those guesses to the correct answer.

D. It looks at the data and makes guesses, then it compares those guesses to the correct answer.

## Explanation

The correct answer is D. It looks at the data and makes guesses, then it compares those guesses to the correct answer.

Artificial neural networks (ANNs) learn by examining data, making predictions or guesses, and then comparing those predictions to the correct answers. Here’s a more detailed explanation of how an artificial neural network learns:

• Data Input: ANNs are fed with input data, which consists of features or patterns that the network needs to learn from. The input data is typically represented as numerical values.
• Forward Propagation: The input data is processed through the network’s layers of interconnected artificial neurons, starting from the input layer and moving through one or more hidden layers to the output layer. Each neuron performs a weighted computation based on the input it receives and applies an activation function to determine its output.
• Prediction or Guess: As the input data propagates through the network, it generates predictions or guesses for the corresponding output values. These predictions are produced by the final layer of the network, which is the output layer.
• Error Calculation: The predicted output is compared to the actual or correct output values. The difference between the predicted output and the correct output is quantified as an error or loss. Various loss functions can be used depending on the nature of the problem, such as mean squared error or cross-entropy loss.
• Backpropagation: Backpropagation is the key mechanism for learning in artificial neural networks. It involves calculating the gradients of the error with respect to the network’s weights and biases. The gradients provide information on how much each weight and bias contributed to the overall error.
• Weight and Bias Update: The gradients obtained through backpropagation are used to update the weights and biases of the artificial neurons in the network. This update is performed using optimization algorithms such as gradient descent or its variants. The objective is to adjust the weights and biases in a way that minimizes the error in subsequent iterations or epochs.
• Iteration and Learning: The process of forward propagation, error calculation, backpropagation, and weight update is repeated iteratively for multiple epochs. During each iteration, the network refines its predictions and adjusts its internal parameters based on the comparison between predicted outputs and correct outputs. Over time, the network learns to make more accurate predictions and minimize the error.

Option A, “A computer scientist programs each neuron to have the correct answer to any question,” is incorrect. ANNs do not have their neurons individually programmed with the correct answers. Instead, they learn to generate predictions by adjusting the weights and biases of the neurons based on the training data.

Option B, “Only correct answers go into the input layer, so it learns what’s correct from the output layer,” is incorrect. ANNs do not receive only correct answers in the input layer. The input layer receives the raw data, and the network learns to associate the input patterns with the corresponding correct outputs during the learning process.

Option C, “The hidden layers hide the incorrect answers from the rest of the network,” is incorrect. Hidden layers in ANNs do not hide incorrect answers. They process the input data and contribute to the network’s ability to learn complex patterns and relationships in the data.

In summary, an artificial neural network learns by looking at the data, making guesses or predictions, comparing them to the correct answers, and adjusting its internal parameters (weights and biases) through backpropagation and optimization algorithms. Through this iterative process, the network gradually improves its predictions and learns to generalize from the provided data to make accurate predictions on unseen data.

## Reference

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