Top 5 Applications of Neural Networks

Neural networks are the result of combining artificial intelligence and brain-inspired design to change current computing. With numerous layers of interconnected artificial neurons, these networks mimic the complicated workings of the human brain, allowing for incredible machine learning feats. There are several varieties of neural networks, ranging from feedforward to recurrent and convolutional, each designed for a distinct task. This article discusses its real-world applications in fields such as image identification, natural language processing, and more.

 

Introduction 

 

A neural network is a machine learning software or model that makes decisions in a similar way to the human brain, employing procedures that imitate how biological neurons collaborate to discover phenomena, consider possibilities, and reach conclusions.

 

Each neural network is made up of three layers of nodes, or artificial neurones: an input layer, one or more hidden layers, and an output layer. Each node links to others and has its own weight and threshold. If the output of any particular node exceeds the defined threshold value, that node is activated and sends data to the next tier of the network. Otherwise, no data is transmitted to the next tier of the network.

 

Artificial neural networks comprise artificial neurones known as units. These units are organized into a succession of layers that collectively form the entire Artificial Neural Network in a system. A layer can have a dozen or millions of units, depending on how complicated neural networks are necessary to understand the dataset's underlying patterns.

 

Neural networks are employed in machine learning, which is a type of computer software that learns without specific instructions. Neural networks are specifically employed in deep learning, a more advanced sort of machine learning that can derive conclusions from unlabelled data without the need for human intervention. For example, a deep learning model constructed on a neural network and fed enough training data may be able to recognise elements in a previously unseen snapshot.

 

Also Read | Convolutional Neural Networks: Explained

 

Kinds of Neural Networks

 

Neural Networks have a variety of types with each of its structure, function and role. In this section, we'll look at some of the most prevalent forms of neural networks and their applications.

 

1. Feedforward Neural Network

 

Feedforward neural networks are the most fundamental type of neural network. They include an input layer, one or more hidden layers, and an output layer. Data moves forward across the network, from the input layer to the output layer.

 

Feedforward neural networks are widely utilized in a range of applications, including image and audio recognition, natural language processing, and predictive modeling. A feedforward neural network, for example, might be used to anticipate a customer's chance of turnover based on previous behavior.

 

2. Perceptron

 

Frank Rosenblatt constructed one of the first types of neural networks, the perceptron, in 1958. It is a single-layer neural network that accepts a set of inputs, processes them, and returns an output.

 

Perceptrons can be applied to a variety of tasks, including image recognition, signal processing, and control systems. However, one disadvantage of artificial neural networks is that they can only handle issues where the data can be divided into two categories using a straight line (known as a linearly separable problem), restricting the network's ability to answer more complex problems.

 

3. Radial Basis Functional Neural Network.

 

A Radial Basis Function (RBF) neural network is a sort of feedforward neural network that converts its inputs into outputs using a set of radial basis functions. Like many neural networks, it is made up of three layers: the input layer, the hidden layer, and the output layer.

 

RBF networks are widely utilized in pattern recognition, classification, and control applications. One of the most common uses for RBF networks is image recognition, where they are used to detect objects in a picture.

 

4. Modular neural networks

 

Modular neural networks (MNN) are neural networks that join and collaborate to tackle complicated issues. In a modular network, each module is a separate network that is intended to tackle a specific subproblem. The outputs of each module are then merged to create a final output.

 

MNNs have been utilized to address numerous complicated issues, including computer vision, speech recognition, and robotics.

 

Also Read | Deep Learning and Neural Networks: Explained

 

Applications of Neural Networks

 

To explain neurons in a simple manner, those are the fundamental blocks of the human brain. All your life experiences, feelings, emotions, basically your entire personality is defined by those neurons. Every decision you make in your daily life, no matter how small or big, is driven by those neurons.

 

Listed below are top 5 applications of neural networks

 

1. Speech Recognition

 

Speech recognition relies heavily on artificial neural networks (ANNs). Earlier speech recognition models used statistical models such as Hidden Markov Models. With the introduction of deep learning, several forms of neural networks have become the only way to acquire a precise classification.

 

2. Adaptive Battery in Android OS

 

If you happen to have an android phone running android os 9.0 or above, when you go inside the settings menu under the battery section you will see an option for an adaptive battery. What this feature does is pretty remarkable. This feature basically uses Convolutional Neural Networks(CNN) to identify which apps in your phone are consuming more power and based on that, it will restrict those apps.

 

3. Face Unlock 

 

Today almost any newly launched android phone is using some sort of face unlock to speed up the unlocking process. Here essentially CNN’s are used to help identify your face. That’s why you can observe that the more you use face unlock, the better it becomes over time.

 

4. Signature Classification

 

We employ artificial neural networks to recognize signatures and categorize them according to the person’s class when developing these authentication systems. Furthermore, neural networks can determine whether or not a signature is genuine.

 

5. Live Caption in Android OS 

 

As a part of Android OS 10.0, Google introduced a feature called Live Caption. When enabled this feature uses a combination of CNN and RNN to recognize the video and generate a caption for the same in real-time

 

Final Words

 

Neural networks extract identifying features from data, lacking pre-programmed understanding. Network components include neurons, connections, weights, biases, propagation functions, and a learning rule. Neurons receive inputs, governed by thresholds and activation functions. Connections involve weights and biases regulating information transfer. Learning, adjusting weights and biases, occurs in three stages: input computation, output generation, and iterative refinement enhancing the network’s proficiency in diverse tasks.

 

In conclusion, we’ve seen the world of Neural Networks, understanding them as networks inspired by the structure of the human brain. We explored their architecture, comprising input, hidden, and output layers, akin to the workings of neurons in our brains. We also delved into the core ingredients of Neural Networks, such as data, the model itself, objective functions, and optimization algorithms, essential for their functioning.