A: The different types of deep learning models are essentially just different kinds of neural networks:
● Feedforward neural network. This network is often referred to as a regular neural network, as it was the first type of neural network to emerge. In this model, information moves in a singular direction — forward. There is no circular nature like in other models, where data loops over and over. The data travels from the input layer, through the hidden layers, and to the output layer. Because they are relatively simple yet powerful, feedforward neural networks are very beginner-friendly. They also have a wide range of applications including speech recognition, image recognition, and data analytics tasks like regression and predictive modeling.
● Convolutional neural network. You may hear this type of network referred to as a CNN or ConvNet. A convolutional network is similar to a feedforward network — except it’s not linear, but circular. Data moves through the model in a constant loop. This gives a CNN a variety of applications, including classifying images.
● Recurrent neural network. This network is recurrent because it has something called a memory state, which means it can store the output of a previous algorithm run and use it to influence a current input and output. This capability is useful in Natural Language Processing (NLP) models.