What is the difference between deep and shallow neural networks?

The main difference between deep and shallow neural networks lies in the number of layers they contain.

Shallow neural networks, also known as single-layer neural networks, consist of only one hidden layer between the input and output layers. The hidden layer performs computations and transforms the input data to produce the desired output. Shallow neural networks are relatively simple and have limited capacity to learn complex patterns and representations.

On the other hand, deep neural networks have multiple hidden layers stacked between the input and output layers. These networks can be quite deep, with tens or even hundreds of layers. Each layer performs computations and passes the transformed data to the next layer. Deep neural networks have a greater capacity to learn intricate features and hierarchical representations from the data, allowing them to capture more complex patterns and relationships.

The advantage of deep neural networks is their ability to learn and represent highly abstract and nuanced features from raw data, leading to improved performance in tasks such as image and speech recognition, natural language processing, and many other domains. However, training deep neural networks can be challenging due to the vanishing or exploding gradient problem, which requires specialized techniques like skip connections, batch normalization, or residual networks to mitigate.