Newbie question about deep learning

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Deep learning is fundamentally based on neural networks characterized by multiple layers, which is what distinguishes it from traditional machine learning. The term "deep" specifically refers to the depth of these networks, meaning the number of layers, rather than the overall size of the network or the computing power required. While increased computing power and memory are essential for implementing deep learning effectively, they are not defining features of the concept itself. The complexity introduced by the numerous layers in deep learning models can complicate the interpretability of how these networks derive their conclusions.
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Is Deep Learning based on Neural Networks (which seem to have been around for decades) and an increase in computing power and memory? Does it utilize very large Neural Networks?
 
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Deep learning is based on Neural Networks that have many layers in the network (see https://en.wikipedia.org/wiki/Deep_learning). The term does not refer to computing power or memory size although both are required to accomplish it. You can study a lot about the theory of deep learning without ever talking about the computer requirements. The "deep" refers to the large number of layers, not necessarily to the overall size of the network. The added layers in the second diagram below is what is meant by "deep". All those layers make it more difficult to understand how the network is arriving at its conclusions when it is working. (from https://quantdare.com/what-is-the-difference-between-deep-learning-and-machine-learning/)
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