The 7 Types of Artificial Neural Networks ML Engineers Need to Know

Neural Networks are networks used in Machine Learning that work similar to the human nervous system. It is designed to function like the human brain where many things are connected in various ways.  Artificial Neural Networks find extensive applications in areas where traditional computers don’t fare too well. There are many kinds of artificial neural networks used for the computational model.

The set of parameters and operations of mathematics determines the type of neural networks to be used to get the result. Here we will discuss some of the critical Neural Networks types in Machine Learning:

Top 7 Artificial Neural Networks in Machine Learning

1. Modular Neural Networks

In this type of neural network, many independent networks contribute to the results collectively. There are many sub-tasks performed and constructed by each of these neural networks. This provides a set of inputs that are unique when compared with other neural networks. There is no signal exchange or interaction between these neural networks to accomplish any task.

The complexity of a problem is easily reduced while solving problems by these modular networks because they completely break down the sizeable computational process into small components. The computation speed also gets improved when the number of connections is broken down and reduces the need for interaction of the neural networks with each other.

The total time of processing will also depend on the involvement of neurons in the computation of results and how many neurons are involved in the process. Modular Neural Networks (MNNs) is one of the fastest-growing areas of Artificial Intelligence.

2. Feedforward Neural Network – Artificial Neuron

The information in the neural network travels in one direction and is the purest form of an Artificial Neural Network. This kind of neural network can have hidden layers and data enter through input nodes and exit through output nodes. Classifying activation function is used in this neural network. There is no backpropagation, and only the front propagated wave is allowed.

There are many applications of Feedforward neural networks, such as speech recognition and computer vision. It is easier to maintain these types of Neural Networks and also has excellent responsiveness to noisy data.

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3. Radial basis function Neural Network

There are two layers in the functions of RBF. These are used to consider the distance of a centre with respect to the point. In the first layer, features in the inner layer are united with the Radial Basis Function. In the next step, the output from this layer is considered for computing the same output in the next iteration. One of the applications of Radial Basis function can be seen in Power Restoration Systems. There is a need to restore the power as reliably and quickly as possible after a blackout. 

4. Kohonen Self Organizing Neural Network

In this neural network, vectors are input to a discrete map from an arbitrary dimension. Training data of an organization is created by training the map. There might be one or two dimensions on the map. The weight of the neurons may change that depends on the value.

The neuron’s location will not change while training the map and will stay constant. Input vector and small weight are given to every neuron value in the first phase of the self-organization process. A winning neuron is a neuron that is closest to the point. Other neurons will also start to move towards the point along with the winning neuron in the second phase.

The winning neuron will have the least distance, and euclidean distance is used to calculate the distance between neurons and the point. Each neuron represents each kind of cluster, and the clustering of all the points will happen through the iterations.

One of the main applications Kohonen Neural Network is to recognize the data patterns. It is also used in the medical analysis to classify diseases with higher accuracy. Data are clustered into different categories after analyzing the trends in the data.

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5. Recurrent Neural Network(RNN) 

The principle of Recurrent Neural Network is to feedback the output of a layer back to the input again. This principle helps to predict the outcome of the layer. In the Computation process, Each neuron will act as a memory cell. The neuron will retain some information as it goes to the next time step.

It is called a recurrent neural network process. The data to be used later will be remembered and work for the next step will go on in the process. The prediction will improve by error correction. In error correction, some changes are made to create the right prediction output. The learning rate is the rate of how fast the network can make the correct prediction from the wrong prediction.

There is much application of Recurrent Neural Networks, and one of them is the model of converting text to speech. The recurrent neural network was designed for supervised learning without any requirement of teaching signal.

6. Convolutional Neural Network

In this type of neural network, Learn-able biases and weights are given to the neurons initially. Image processing and signal processing are some of its applications in the computer vision field. It has taken over OpenCV. 

The images are remembered in parts to help the network in computing operations. The photos are recognized by taking the input features batch-wise. In the computing process, image is converted to Grayscale from HSI or RGB scale. The classification of images is done into various categories after the image is transformed. Edges are detected by finding out the pixel value change.

The technique of Image classification and signal processing are used in ConvNet. For image classification, Convolutional Neural Networks have a very high level of accuracy. That is also the reason why convolutional neural networks are dominating the computer vision techniques. Prediction of yield and growth in the future of a land area are other applications of convolutional neural networks in weather and agriculture features.

7. Long / Short Term Memory

Schmidhuber and Hochreiter in 1997 built a neural network which is called long short term memory networks (LSTMs). Its main goal is to remember things for a long time in a memory cell that is explicitly defined. Previous values are stored in the memory cell unless told to forget the values by “forget gate”.

New stuff is added through the “input gate” to the memory cell, and it is passed to the next hidden state from the cell along the vectors which is decided by the “output gate”. Composition of primitive music, writing like Shakespeare, or learning complex sequences are some of the applications of LSTMs.

Advantages and Disadvantages of Artificial Neural Networks

We have read about the 7 types of Artificial Neural Networks that all Machine Learning engineers much know about. However, you should also be aware of the advantages and disadvantages before working with these ANN types –

Advantages – 

• Memory Distribution

Determining the instances and motivating the network in conformity with the intended output by showing these examples is crucial for ANN types and capable of adapting. The network’s result can be false if the event can’t be represented by the network in all of its characteristics because the network’s succession is directly related to the selected occurrences.

• Storing the Data

Traditional programming does not employ a database; instead, it stores all of the data on the entire network. The network continues to function even if some data disappears from one location temporarily.

• Multifunctional

Different types of ANN can perform multiple tasks at the same time without any disruptions.

• Fault Tolerance

The network is fault-tolerant since the removal of one or more ANN cells does not prevent the network from producing output.

• Works with incomplete or less knowledge

Following ANN training, the data may still produce output even with insufficient data. The relevance of the missing data in this situation is what causes the performance loss.

Disadvantages – 

• Depends on hardware

According to their format, artificial neural networks require processors with simultaneous processing capacity. As a result, the equipment’s execution is dependent.

• No proper structure

The construction of artificial neural networks is not determined by any specific rules. Through expertise, trial, and error, the right network model is achieved.

• No Time Limit

The network is limited to a particular parameter, and this error value may not produce the best outcomes for us.

• Difficulty in Solving Non-Numerical Problems 

Artificial Neural Network types can process data that is numerical or as numerical values. Before using ANN, problems must be transformed into numerical values. The presentation mechanism that must be decided here will directly impact the network’s performance. It is highly dependent on the user’s skills.

• Network Behaviour is Unrecognised

It’s one of the most important ANN concerns. When an ANN generates a testing solution, it doesn’t explain why or how. It diminishes network confidence.

Strategies for Training Artificial Neural Network Types

While working with types of Artificial Neural Networks, Machine Learning Engineers must learn how to train the programme for better performance. Here are some training strategies that can be of help when working along varying types of ANN-

• Reinforcement – This approach is based on research and observation. The ANN decides by monitoring its surroundings. If the observation is unfavourable, the network changes its weights so that it can make a suitable decision on the next try.
• Supervised – It calls for a teacher who is more knowledgeable than the ANN itself. For instance, you may provide certain sample information for which you already know the solutions. This will help you determine how well ANN is working. If ANN comes up with the wrong solutions, you must input the correct one so that the ANN can make the observations and change the answer to the one you want. It will then come up with similar outcomes for your future questions as well.
• Unsupervised – When there isn’t an example data set with known solutions, unsupervised learning is necessary. For instance, looking for a concealed pattern. In this instance, clustering a set of components into groups in accordance with some unidentified pattern is carried out using the available data sets.

Conclusion

These are the different types of neural networks that are used to power Artificial Intelligence and machine learning. We hope this article has shed some light on Neural networks and the types being used for ML.

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