Neural Network

Overview

Main Types of Neural Networks and its Applications — Tutorial. A tutorial on the main types of neural networks and their applications to real-world challenges.

Nowadays, there are many types of neural networks in deep learning which are used for different purposes. This article goes through the most used topologies in neural networks, briefly explains how they work, and covers some of their real-world applications.

Figure 1:
Main types of neural networks. Join us | Towards AI Members | The Data-driven Community

N owadays, there are many types of neural networks in deep learning which are used for different purposes. In this article, we will go through the most used topologies in neural networks, briefly introduce how they work, along some of their applications to real-world challenges.

Figure 2:
The perceptron: a probabilistic model for information storage and organization in the brain.

This article is our third tutorial on neural networks; to start with our first one, check out neural networks from scratch with Python code and math in detail.

Figure 3:
Representation of the perceptron (p).

The perceptron model is also known as a single-layer neural network. This neural net contains only two layers:

• Input Layer
• Output Layer

In this type of neural network, there are no hidden layers. It takes an input and calculates the weighted input for each node. Afterward, it uses an activation function (mostly a sigmoid function) for classification purposes.

Applications:

• Classification.
• Encode Database (Multilayer Perceptron).
• Monitor Access Data (Multilayer Perceptron).

A feed-forward neural network is an artificial neural network in which the nodes do not form a cycle. The perceptrons are arranged in layers where the input layer takes in input, and the output layer generates output. The hidden layers have no connection with the outer world; that’s why they are called hidden layers. Each layer is fully connected to the next.

There are no back-loops. Back propagation is generally used to update the weight values.

Applications:

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

Radial basis function networks are generally used for function approximation problems. They use a Radial Basis Function as an activation function. They are well-suited for continuous value outputs. RBIs determine how far the generated output is from the target output. RBNs behave as FF networks with different activation functions.

Applications:

• Function Approximation.
• Time series Prediction.
• Classification.
• System Control.

A deep feed-forward network uses more than one hidden layer. More hidden layers can reduce overfitting and improve generalization in some cases.

Applications:

• Data Compression.
• Pattern Recognition.
• Computer Vision.
• ECG Noise Filtering.
• Financial Prediction.

RNNs process sequences by incorporating time-delayed inputs in hidden layers. They can remember information over time but may be slow to train and have limited memory for long-range dependencies.

Applications:

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

LSTM networks introduce memory cells to handle long-range dependencies. They can remember data over longer sequences than standard RNNs.

Applications:

• Speech Recognition.
• Writing Recognition.

GRUs are a variation of LSTMs with three gates and no separate cell state.

Applications:

• Polyphonic Music Modeling.
• Speech Signal Modeling.
• Natural Language Processing.

An autoencoder is an unsupervised model that learns to compress data and reconstruct it. Encoders convert input data to lower dimensions; decoders reconstruct the compressed data.

Applications:

• Classification.
• Clustering.
• Feature Compression.

A Variational Autoencoder uses a probabilistic approach to describe observations and their…