Introduction to Artificial Neural Network

So far, we came across various tools, algorithms that help Machine Learning Models to think human-like. To perform all the decision-making tasks as well as for processing the data. We also came across two of the most important neural networks namely, CNN and RNN. While we predominantly use one for image processing, the other works wonder on sequential data processing.

However, the human brain doesn’t just do these two tasks. It is capable of achieving much more than just interpreting its surroundings. So, how are we going to train these Machine Learning models to think like humans and make logical decisions not only regarding images and texts but much more than that?

PythonGeeks brings to you, yet another fascinating Neural Network to understand Machine Learning a little better. In this article, we will discuss the most trending Neural Network that we use chiefly in developing and training models that have human-like thinking abilities- Artificial Neural Networks. This article from PythonGeeks will help you understand the basics of what actually Artificial Neural Network is. It will guide you through the working, applications, and types of ANN. Hence, let us dive straight into the introduction part and see what ANN is all about.

Introduction to Artificial Neural Network

As you are reading this article, what part of your consciousness is interpreting all this information? The Brain! The Human brain takes in its surroundings through various receptors like sensory, visionary, and others. The Brain then processes this information to make beneficial decisions that are accurate. Inspired by this functioning of the Brain, the Artificial Neural Network was developed in the early 1970s to help Machines work like human brains.

Artificial Neural Networks or commonly referred to as Neural Networks or ANN, are nonlinear statistical models that display a complex relationship between the provided input and the consequent output to discover a similar pattern. These are computational networks inspired by a biological neural network that forms the structure of the human brain.

Similar to how humans have a network of neurons that are interconnected with each other to process all the information that the receptors provide to them, programmers build ANN using nodes which work like neurons. A typical brain has a Neural Network that consists of Dendrites, Cell nucleus, Synapse, and Axon that forms the biological neural network.

Artificial Neural Network is made from biological network counterparts namely, Inputs, Nodes, Weights, Output. These entities assist the neural network to have a human-like understanding of its surroundings. In an attempt to help machines make human-like decisions, ANN could be thought of as a network of neurons working as parallel processors to make “Learning” easy for the Machines.

In order to understand the ANN model better, we will look into the architecture of the ANN and understand how this coordination will work. The three main building blocks of the ANN structure are the activation functions, learning rules, and interconnections. Before learning about the architecture, let us take a quick look at one of the most important aspects of ANN- interconnections.

Interconnections

Interconnections are simply the way in which the processing units of the network or the Neurons of the network connect with each other. Since the results of the ANN mainly depends on the working of these Neurons, studying about their arrangement and geometry in the network becomes important.

These arrangements are what make up the architecture of the network. Thus, they contain a single input as well as output layer for the generation of the results. This arrangement makes the working neurons invisible to the developers acting as a black box for them. Increasing these hidden layers of neurons can help one in achieving results with high precision and reliability.

Architecture of Artificial Neural Network

To understand the working of the neural network, we first need to be familiar with the basic components of the network that builds the system.

In an attempt to understand the concept of neural networks, we can imagine the network as a connection between millions of artificially created neurons. These neurons also have interconchange mechanisms associated with them. These neurons or nodes are termed units that are arranged continuously in a sequence of layers.

Understanding this architecture is quite important since it helps in determining the results of the network. The simplest form of the structure includes a single input and output layer which results in linear regression. The addition of a hidden layer enhances the predictiveness of the overall network’s manifold. This generalization also helps in overcoming the problem of overfitting.

Let us understand the structure of these layers in detail. The Artificial Neural Network mainly comprises 3 layers, namely input layer, hidden layer, and output layer.

1. Input Layer

The first or primary layer of the ANN is the input layer. In a biological neural network, the brain receives the input in the form of sensations received from the receptors. Similar to this, the input layer receives the input data in the form of image pixels, texts, audio files, and binary data.

2. Hidden Layer

After receiving the signal from the receptors, the information is sent to the brain for processing. Similarly, in ANN, the received information then traverses to the Hidden Layer for processing. The network may consist of a single input layer as in the case of perceptron or a series of multiple hidden layers. The task of these layers is to perform various mathematical computations on the raw data. The results of these layers are the patterns that the network identifies after processing them.

3. Output Layer

After the rigorous processing of the data in the hidden layers, the information is sent to the output layer. The various parameters that we feed to the model, affect the performance of the model. Hence, the output of the ANN model is generally dependent on the input parameters. Examples of these parameters may include weights, biases, learning rate and others.

Working of ANN

The working of the Artificial Neural Network could be best understood with the help of a directed graph. The artificial neurons are the nodes of the graph, the association between the neuron input and output are the edges. ANN receives information in the form of patterns and images in the form of vectors.

Each node of this graph has a weight associated with it. You can think of these weights as the measure of strength between connections of the neurons in the network. The computing unit then sums up all the weighted units. If this sum results out to be zero, then we add a bias to receive a non-zero output. However, these results can vary vastly resulting in the output reaching an infinite value. In order to avoid this, we decide on a maximum threshold value prior to implementation. Now, this total sum of weighted nodes traverses to the activation function.

Activation functions are a set of pre-designed mathematical functions used to achieve the desired output. There are a large number of activation functions but we mainly use linear or non-linear to classify them. Some of the most used activation functions are Binary (returns either the input or 0), Sigmoid Hyperbolic (makes use of tan hyperbolic function to depict the output).

Types of Artificial Neural Network

There are two major types of Artificial Neural networks that are used are currently being trending, namely

• FeedForward Neural Network
• FeedBack Neural Network

1. FeedForward Neural Network

FeedForward ANNs deals with the unidirectional flow of the data. That means, in feedforward ANN, the data flow from the input layer to the hidden layer and then finally to the output layer without any branching or backtracking. Feedback loops are absent in this network. These networks are predominantly used for Supervised Machine Learning algorithms like Classification, Regression, Image Recognition and so on. This network is useful when we want to deal with data that is not sequential.

2. Feedback Neural Network

As opposed to the case of FeedForward networks, Feedback networks have a feedback loop present in them. These networks are beneficial in cases where memory retention is a key factor. The prime usage of these can be seen in the form RNN. These are mainly used with problems that deal with the sequential data type.

Advantages of ANN

1. Parallel processing: Since ANN has a large number of artificial neurons that work parallelly, these models are quite efficient in handling multiple tasks simultaneously.

2. Data Storage: In such networks, the data is dispersed on the entire network rather than storing it on a particular database. This helps in the convenient flow of working of the algorithm even if some piece of information gets lost or corrupted.

3. Ability to work with a scarcity of Information: After the ANN model is trained adequately, the model is able to produce an output even when the information is inadequate.

4. Memory Distribution: SInce ANN is known for producing accurate results in a really short span, it sometimes becomes troublesome in achieving the results. Since the information distributes itself over the whole network, the network may sometimes tend to produce false output according to its training.

Disadvantages of ANN

1. Requirement of Diversity: Though ANN has the ability to work with both linear as well as non-linear data, it requires a huge amount of diversely trained data in order to cater to real-life problems. This issue occurs since any learning model requires an abundance of trained data in order to function effectively.

2. Space Scarcity: Though ANN provides you with the amazing feature of processing the data even when some of the factors are missing, it requires a huge chunk of space over the physical memory device. This causes scarcity of storage space for other processes that run simultaneously over a period of time.

3. Lack of Insight: Though ANN is known for providing accurate and reliable results, the implementation of the process is, however, is hidden from the developer. This makes it very difficult for the programmer to track the progress of the network since we directly get the output after providing the input.

4. Duration of Network: Since we train the model in such a way that the possibilities of errors are restricted to a specific limit, it avoids the chances of giving us optimal results.

5. Selection of the Structure: Since ANN is susceptible to simple network structures, we cannot conclude that the structure that we demonstrate now is the compatible solution for the process. We can only conclude this by trying and obtaining the results of different structures.

Bayesian Network

This network structure makes use of Bayesian Interference in order to calculate the probabilistic graph of the outcomes of the network. We also know these networks by the name of Belief Networks. The main aim of the network is to connect the random variables with the edges for predicting the probabilistic dependencies. We program the network in such a way that if one of the variables affects the other one then the edge that connects them is a straight line.

The higher the probability of the nodes of the edge, the stronger is the edge that connects them. On this basis, we are able to find the relationship and other information about the random variables of the training dataset.

We also call these networks DAG (Directed Acyclic Graphs), since they face the constraint of not returning the nodes through directed arcs making them acyclic. Bayesian Networks have two dimensions namely, the range of prepositions and probability associated with each proposition. The primary use of Bayesian Networks is in the field of prediction and understanding the relationship between the variables for output prediction.

Applications Of ANN

Because of the vast arena of the advantages of ANN, it is used in a large number of areas such as

1. Image Processing and Character Recognition

Given the amazing ability of ANN to take in huge amounts of input and process it discovers hidden as well as complex, non-linear relationships, ANNs are widely used for image and character recognition.

Many industries are deploying ANNs for fraud detection since it produces accurate image recognition results. ANNs also find their use in other image recognition fields like satellite imagery, facial recognition, and the most fascinating one being self-driving cars. This also makes ANN useful in handwriting recognition. We can train ANN models to accurately differentiate and authenticate signatures with reliable results.

2. Forecasting

Nowadays, forecasting has become the crux of many industries. We require forecasting extensively in business decisions, economic and monetary policies as well as in finance and stock marketing. Most of the time, the predictions are hard to detect as in the case of stock price prediction. In such cases, where the traditional forecasting models are of no use, we can deploy ANN-trained models to do accurate predictions.

3. Aerospace

Due to the effective ability to establish a non-linear relationship between input and output, ANNs find huge applications in Aerospace Engineering. You can train ANN models to perform fault diagnosis, high-performance auto-piloting, securing aircraft controls, and modeling key dynamic simulations.

4. Speech Recognition

ANN is a very useful tool when our problem deals with the recognition of Speech. Conventionally, programmers used the Hidden Markov method for Speech Recognition. However, with the advancement in neural networks, programmers are now trying to deploy ANN models for accurate and reliable speech recognition results.

Conclusion

We have thus reached the end of the article. In this article, we came across the various basics of the widely used Artificial Neural Networks. Hope that this article helped you in gaining key information about the working of the model. You will now be able to understand how programmers are able to train machines to think like humans.