Question 1

What is a neural network?

Accepted Answer

A neural network is a computational model inspired by the structure of the human brain. It consists of interconnected layers of nodes (neurons) that process information by passing signals through weighted connections, enabling the network to learn patterns and make predictions from data.

Question 2

How do neural networks learn?

Accepted Answer

Neural networks learn by adjusting the weights of connections between neurons based on how far their predictions are from the correct answers. This process uses training data and an optimisation algorithm to gradually minimise the error, allowing the network to classify data more accurately over time.

Question 3

What is backpropagation?

Accepted Answer

Backpropagation is the primary algorithm used to train neural networks. It calculates the gradient of the error with respect to each weight by propagating the error backwards from the output layer to the input layer, then uses this information to update the weights and improve the network's accuracy.

Question 4

What are common types of neural networks?

Accepted Answer

Common types include the Perceptron (simplest single-layer network), feedforward networks (multi-layer networks for classification), convolutional neural networks (CNNs, used for image recognition), and recurrent neural networks (RNNs, used for sequential data like text and speech).

🧪 Neural Network Visualizer - Backpropagation, Gradient Descent and Machine Learning

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