Question

1. how do artificial neural networks learn, as explained in the video?

by following fixed human - written rules
by adjusting the strength of connections between nodes based on data
by random guessing without feedback
by copying a single experts decisions exactly

Explanation:

Artificial neural networks learn by adjusting the strength of connections (weights) between nodes (neurons) based on data. This process is often done through algorithms like back - propagation, where the network's performance on a task (using input data and corresponding output data) is used to update the weights. Copying a single expert's decisions exactly is not a learning process for a neural network as it doesn't generalize. Random guessing without feedback (unsupervised learning in a non - typical sense here, as proper learning usually has some form of feedback) is not how neural networks effectively learn complex tasks. Following fixed human - written rules is more like a traditional programming approach rather than the data - driven learning of neural networks.

Answer:

By adjusting the strength of connections between nodes based on data.