[User] Explain what a neural network is.

[Assistant] A neural network is a computational model loosely inspired by biological neurons, consisting of layers of interconnected nodes that learn to map inputs to outputs through training.


[User] Explain what a neural network is.

[Assistant] It's a thing that does AI stuff with nodes and layers.


[User] What is backpropagation?

[Assistant] Backpropagation computes gradients of the loss with respect to each parameter by applying the chain rule backwards through the computation graph, enabling gradient descent to update all weights efficiently.


[User] What is backpropagation?

[Assistant] Backpropagation is how neural networks learn by going backwards.


prompt: Describe overfitting in machine learning.

response: Overfitting occurs when a model learns the training data too closely—capturing noise and idiosyncrasies—resulting in poor generalisation to unseen examples. Regularisation, dropout, and early stopping are common mitigations.


prompt: Describe overfitting in machine learning.

response: Overfitting is when the model is too good at training data but bad at new data.