The following article provides a comparison on Deep and Shallow Neural Networks.
To emphasize, Deep neural networks (DNNs) and shallow neural networks (SNNs) are two different types of neural networks used in artificial intelligence and machine learning.
While, Shallow neural networks have a limited number of hidden layers, usually one or two. Also, they are are best suited for simple problems where the relationship between inputs and outputs is relatively straightforward. Moreover, SNNs are faster to train and require less computational power compared to DNNs. However, they are not as effective in complex tasks where there are many non-linear interactions between inputs and outputs.
On the other hand, Deep neural networks, may have many hidden layers and can model complex relationships between inputs and outputs. Furthermore, DNNs are able to learn hierarchical representations of data. Also, they can automatically extract useful features from raw data. This makes them suitable for solving complex problems such as image and speech recognition, natural language processing, and recommendation systems. However, DNNs require much more computational power and time to train, compared to SNNs.
Summary – Deep and Shallow Neural Networks
In summary, shallow neural networks are faster and simpler. Whereas deep neural networks are more powerful. However, they are slower. Also, they require more computational resources. Therefore, the choice between a shallow or deep neural network will depend on the nature of the problem being solved and the available computational resources.
Further Reading
Deep Learning Practice Exercise
Deep Learning Methods for Object Detection
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