Types of Deep Learning Networks

1. Feed Forward Neural Network

A feed-forward neural network is none other than an Artificial Neural Network, which ensures that the nodes do not form a cycle. In this kind of neural network, all the perceptrons are organized within layers, such that the input layer takes the input, and the output layer generates the output. Since the hidden layers do not link with the outside world, it is named as hidden layers. Each of the perceptrons contained in one single layer is associated with each node in the subsequent layer. It can be concluded that all of the nodes are fully connected. It does not contain any visible or invisible connection between the nodes in the same layer. There are no back-loops in the feed-forward network. To minimize the prediction error, the backpropagation algorithm can be used to update the weight values.

Applications:

• Data Compression
• Pattern Recognition
• Computer Vision
• Sonar Target Recognition
• Speech Recognition
• Handwritten Characters Recognition

2. Recurrent Neural Network

Recurrent neural networks are yet another variation of feed-forward networks. Here each of the neurons present in the hidden layers receives an input with a specific delay in time. The Recurrent neural network mainly accesses the preceding info of existing iterations. For example, to guess the succeeding word in any sentence, one must have knowledge about the words that were previously used. It not only processes the inputs but also shares the length as well as weights crossways time. It does not let the size of the model to increase with the increase in the input size. However, the only problem with this recurrent neural network is that it has slow computational speed as well as it does not contemplate any future input for the current state. It has a problem with reminiscing prior information.

Applications:

• Machine Translation
• Robot Control
• Time Series Prediction
• Speech Recognition
• Speech Synthesis
• Time Series Anomaly Detection
• Rhythm Learning
• Music Composition

3. Convolutional Neural Network

Convolutional Neural Networks are a special kind of neural network mainly used for image classification, clustering of images and object recognition. DNNs enable unsupervised construction of hierarchical image representations. To achieve the best accuracy, deep convolutional neural networks are preferred more than any other neural network.

Applications:

• Identify Faces, Street Signs, Tumors.
• Image Recognition.
• Video Analysis.
• NLP.
• Anomaly Detection.
• Drug Discovery.
• Checkers Game.
• Time Series Forecasting.

4. Restricted Boltzmann Machine

RBMs are yet another variant of Boltzmann Machines. Here the neurons present in the input layer and the hidden layer encompasses symmetric connections amid them. However, there is no internal association within the respective layer. But in contrast to RBM, Boltzmann machines do encompass internal connections inside the hidden layer. These restrictions in BMs helps the model to train efficiently.

Applications:

• Filtering.
• Feature Learning.
• Classification.
• Risk Detection.
• Business and Economic analysis.

5. Autoencoders

An autoencoder neural network is another kind of unsupervised machine learning algorithm. Here the number of hidden cells is merely small than that of the input cells. But the number of input cells is equivalent to the number of output cells. An autoencoder network is trained to display the output similar to the fed input to force AEs to find common patterns and generalize the data. The autoencoders are mainly used for the smaller representation of the input. It helps in the reconstruction of the original data from compressed data. This algorithm is comparatively simple as it only necessitates the output identical to the input.

• Encoder: Convert input data in lower dimensions.
• Decoder: Reconstruct the compressed data.

Applications:

• Classification.
• Clustering.
• Feature Compression.