Shallow neural networks refer to networks that contain only one or two layers of hidden neurons between the input and output layers. These models are simpler and typically work well for simpler tasks where feature relationships are not highly complex. However, they may struggle to learn intricate patterns in large or high-dimensional datasets.
Deep neural networks (DNNs), in contrast, consist of multiple hidden layers, allowing them to learn hierarchical feature representations at different levels of abstraction. These networks are powerful tools for tasks such as image recognition, natural language processing, and speech recognition, where the data is complex and requires multi-level feature extraction.
The main difference is the number of hidden layers: shallow networks are simpler with fewer layers, while deep networks can model more complex relationships due to their deeper structure.