Deep neural networks are a subset of artificial neural networks. They have multiple hidden layers between the input and output layers. These layers enable them to learn more complex representations of input data, making them highly effective for tasks such as image and speech recognition, natural language processing, and game playing.

There are several types of deep neural networks, each with its own unique architecture and applications. A few among them are listed below:

• Feedforward Neural Networks (FFNNs): These are the most basic type of neural network, consisting of an input layer, one or more hidden layers, and an output layer. They are designed to process data in a forward direction, with no feedback loops. As there is no backpropagation,  the weights cannot be updated.
• Convolutional Neural Networks (CNNs): These are specialized deep neural networks that are highly effective for image and video recognition tasks.These networks have multiple convolution layers which help in extracting features from the image. The filters used in these layers are updated using backpropagation. After the convolutional layer, pooling layers aggregate the produced maps, and dropout layers can be added for regularization. ReLU is used as the activation function in hidden layers, Softmax for classification and ReLU for regression in the last fully connected dense layer.
• Recurrent Neural Networks (RNNs): RNNs are a type of neural network that is designed to process sequential data. Unlike feedforward neural networks, RNNs can retain information from previous inputs, allowing them to model the temporal dynamics of the data. This ability to remember past information makes RNNs particularly useful for natural language processing and speech recognition tasks.  However, RNNs suffer from the vanishing gradient problem.
• Generative Adversarial Networks (GANs): These are a type of deep neural network used in unsupervised learning that is designed for generative tasks, such as generating realistic images or synthesizing music. GANs consist of two neural networks, Generator and Discriminator, that are trained together in a competitive process, with one network generating new data samples and the other network trying to distinguish between the generated samples and real data.

These are just a few examples of the different types of deep neural networks that are available.