What are the types of neural networks?

Neural networks or simulated neural networks comprise layers of nodes: the input, hidden, and output layers. Each node is assigned weight. In neural networks, we aim to train the model with the data so that there should be a minimum error while predicting the unseen state. We perform forward propagation and calculate the error, and based on that error, we propagate backward through the neural network to minimize the error and update the weights. Neural networks have enormous applications in the field of deep learning.

Types of neural networks

Feed-forward neural network

The feed-forward neural network is also referred to as a multi-layer perceptron. These are the simple neural networks that are comprised of the input layer, hidden layers, and output layers. In the feed-forward neural network, we will increase the number of hidden layers if we want our network to solve complex problems. The hidden layers and the number of neurons are primarily chosen based on the hit-and-trial method.

Convolutional neural network

Unlike the feed-forward or fully connected layers, the weights parameters are shared in a convolutional neural network, making it less complex. We use a convolutional neural network to convolve images with filters to extract the meaning information. We use convolutional neural networks in image segmentation and recognition.

Note: If you want to learn more about the convolution then refer to this link.

Recurrent neural network

Recurrent neural networks also share parameters across the layers of the neural network. These models are used to generate the sequential output. The main principle of these types of neural networks is that it saves the previous step's output and makes it the input for the next step. It keeps the memory of the previously generated output. Long short-term memory and gated recurrent units are also types of recurrent neural networks.

Autoencoders

Autoencoders are the types of neural networks in which we compress the data such that the noise is eliminated from the data, and then we reconstruct or decode the image from the encoded state. In compressing state, it keeps reducing the data dimensions such that our model should not learn the noise that might affect its accuracy. There are four parts of autoencoder which are encoder, bottleneck, decoder, and reconstruction loss.

Generative adversarial network

Generative adversarial neural networks consist of two neural networks: a classifier and a generator.  The generator produces content so accurately that the discriminator cannot differentiate it from the actual content. Concurrently, the discriminator improves its accuracy in recognizing real and fake content.

Conclusion

There are various types of neural networks. A neural network is chosen based on the dataset type and the task required. For example, we use the convolutional neural network if the task and dataset are related to images. Similarly, if the task is related to generating the sequential output, like generating the text, then we will use the recurrent neural network. In short, every neural network has its capabilities and limitations as well.