Neural network application

 

Neural network application

Neural network application

The different types of neural networks are like

1. Neural convolution network
2. Feedforward neural network
3. Recurrent neural network
4. Multilayer Perceptron

The most widely used neural network model is the Convolution Neural Network (CNN).

Artificial neural networks, Let's first look at artificial neural networks (ANN).

 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 inputs of the characteristics. In addition to 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. The skew is responsible for the transfer of the line or curve from the origin.

2. Hidden layer: Hidden layers are the layers that are between the input and output layers. The number of hidden layers can be varied depending on the application and need. Deep neural networks are those that contain more than one hidden layer.

3. Output layer: The output layer contains neurons responsible for the output of the classification or prediction problem. The number of neurons it contains is based on the number of output classes.

Applications of neural networks

Artificial neural networks are widely used in fields like image classification or tagging, or signal detection or language translation like one we find like Google Translator. Whether it is a counterfeit detection using some biometric signal or signal or some kind of forecast or prediction, you can find all of these things that are covered under the umbrella of artificial neural networks.

We can broadly classify applications in the following domains:

1. Images
2. Signs
3. Language

1. ANN in pictures

 Today, artificial neural networks are widely used in images and videos. We can find the applications of neural networks from image processing and classification to even image generation. Image and video tagging are also applications of neural networks. Today's artificial neural networks are also widely used in biometrics such as facial recognition or signature verification.

Character recognition:

Character recognition widely used neural network application that falls under the category of pattern recognition. Images from old documents or literature can be digitized using character recognition. Here, the scanned images of the documents are fed into 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 the backpropagated neural network.

Image Classification or Tagging:

 How nice it feels when we can't recognize something and use Google Image Search! That is exactly what is called image classification or tagging the images that are sent to you. Convolution neural network or feedback neural network with backpropagation is generally used for image classification. There are many other models as well, but it is necessary to select a model based on the data set for the training and characteristics of interest. Transfer learning can be done using any pre-trained model if your problem data set is similar to the pre-trained model data set 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 the image based on that. You need a large set of training data with all the coordinates of the object of interest clearly specified. The most widely used object detection models are YOLO (Just Look Once) and SSD (Single Shot Object Detectors).

Imaging - Imaging builds help generate dummy images based on the data. The generation of cartoons can also be considered one of its applications. GANs (Generative Adversarial Networks) are used for imaging models. They are part of the 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: 

the speech recognition system converts speech signals and decodes them into text or some form of meaning. We can say that it is a direct example of applications in virtual assistants or chatbots. Today, most of us are familiar with Google's smart home, Alexa, Siri, Google assist, or Cortana.

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 these types of tasks. Named entity recognition and speech part tagging are some of the applications that fall within the domain of natural language processing (NLP). The most widely used models are recurrent neural networks (RNN) and short and long-term memory networks (LSTM).

 Although CNN is also used for some of the applications.

Language generation and document summarization: 

Natural language generation and document paraphrasing and summarization are widely used to generate the documents and summarize various documents. Its applications can be found in generating text-based reports from data tables, writing automated reports, summarizing medical reports, generating stories and jokes, etc.

The model that is widely used for text generation is the Recurrent Neural Network (RNN) model.