Introduction to Application of Neural Network
The following article provides an outline for the Application of Neural Network in detail. The first question that arises in our mind is what is meant by an Artificial Neural Network? And why do we need an Artificial Neural Network? Artificial Neural Networks are computational models based on biological neural networks. They make problem-solving easier while conventionally we need to write long code for complex problems.
Neural Networks help to solve the problems without extensive programming with the problem-specific rules and conditions. They are simplified models used for many similar kinds of problems with most complex mathematical computation as behind the scenes. Neural networks are much faster in prediction after it has been trained than a conventional program.
The different types of neural networks are like
- Convolution Neural Network
- Feedforward Neural Network
- Recurrent Neural Network
- Multilayer perceptron
The most widely used neural network model is Convolution Neural Network (CNN).
Artificial Neural Networks
Let us first see Artificial Neural Networks (ANN) first. There are mainly three layers in artificial neural networks.
1. Input Layer: The input layer is the one that contains neurons that are responsible for the feature inputs. In addition to the neurons for features, there is also a neuron for bias added to the input layer. So there are n+1 neurons in total in the input layer. Bias is responsible for the transfer of the line or curve from the origin.
2. Hidden Layer: The hidden layers are the layers that are between input and output layers. The number of hidden layers can be varied based on the application and need. Deep Neural Networks are the ones that contain more than one hidden layer.
3. Output Layer: The output layer contains neurons responsible for the output of a classification or prediction problem. The number of neurons in it is based on the number of output classes.
Applications of Neural Networks
Artificial Neural Networks are widely used in fields like image classification or labelling, or the signal detection or translation of languages as one we find like Google Translator. May it be spoof detection using some biometric or signal or some kind of forecasting or prediction, you can find all these things to be covered under the umbrella of Artificial Neural Networks.
We can widely classify the applications in the following domains:
1. ANN in Images
Artificial Neural Networks are widely used in images and videos currently. We can find the applications of neural networks from image processing and classification to even generation of images. Image and video labelling are also applications of neural networks. Nowadays, artificial neural networks are also widely used in biometrics, like face recognition or signature verification.
- Character Recognition: We must have found the websites or applications that ask us to upload the image of our KYC documents, right? All they do is recognise the characters in those images of our KYC documents. This is a widely used application of neural network that falls under the category of pattern recognition. The document images or old literature can be digitized using character recognition. Here the scanned images of documents are fed to the model, and the model recognizes the textual information in that scanned document. The models that are generally used for this are CNN or other multilayer neural networks like neural network with backpropagation.
- Image classification or labelling: How nice it feels when we can’t recognise something, and we use Google image search!! That is exactly what is called image classification, or it labels the images that are fed to it. Convolution Neural Network or Feedforward neural network with backpropagation is generally used for image classification. There are many other models also, but one needs to select a model based on the dataset for training and features of interest. Transfer learning can be done using any pre-trained model if the dataset of your problem is similar to the dataset of the pre-trained model that you are choosing. There are many pre-trained image classification models that are trained on millions of images of different hundreds and thousands of classes. Some of the models are ResNet, GoogLeNet, InceptionV3, VGG16, ImageNet and many more are available.
- Object detection: Object detection from images is widely used to detect any object and classify it based on that. It needs large training dataset with all the coordinates of the object of interest clearly specified. The widely used object detection models are YOLO (You Only Look Once) and SSD (Single Shot Object Detectors).
- Image Generation: The image generations helps in generating fake images based on the data. Caricature generation can also be considered one of its applications. GAN (Generative Adversarial Networks) are used for image generation models. They comprise of image generator and discriminator.
2. ANN in Signals
ANN is a system based on a biological neural network, one of the types of neurons in ANN is –
- Speech Recognition: Speech recognition system converts the speech signals and decodes them to text or some form of meaning. We can say its a direct example of applications in virtual assistants or chatbots. Nowadays, Google smart home, Alexa, Siri, Google assistance or Cortana are known to most of us.
3. ANN in Language
This can be divided into two models mainly as –
- Text Classification and Categorization: Text classification is an essential part of document search and filtering, online web searches and language identification and sentiment analysis. Neural networks are actively used for this kind of task. Named entity recognition and parts of speech tagging are some of the application that comes under the domain of Natural Language Processing (NLP). The widely used models are Recurrent Neural Networks (RNN) and Long-Short Term Memory (LSTM) networks. Though CNN is also used for some of the applications.
- Language Generation and Document Summarization: Natural Language Generation and Paraphrasing and document summarization are widely used to generate the documents and summarize multi-documents. Their applications can be found in generating text-based reports from data tables, automated reports writing, summarizing medical reports, generating stories and jokes, etc.
The model that is widely used for text generation is the Recurrent Neural Network (RNN) model.
Neural Networks helps to make difficult problems easy through extensive training. They are widely used for classification, prediction, object detection and generation of images as well as text.
This has been a guide to Application on Neural Network. Here we also discuss the introduction on the application of neural network. You may also have a look at the following articles to learn more –
- Classification of Neural Network
- Network Scanning Tools
- Recurrent Neural Networks (RNN)
- Machine Learning vs Neural Network
- TensorFlow RNN | How RNN Works?