What are shallow and deep networks?

Shallow and deep networks are two types of neural networks commonly used in machine learning. Shallow networks have a small number of hidden layers between the input and output layers, making them simpler and faster to train but potentially limited in their ability to learn complex patterns. Deep networks, on the other hand, have multiple hidden layers, allowing them to learn more intricate relationships in the data. While deep networks may be more computationally expensive and time-consuming to train, they have shown to outperform shallow networks in tasks such as image and speech recognition.

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