Artificial neural networks, or simply known as neural networks, are computing systems formed by artificial neurons. These neural networks are inspired by the biological neural network of the animal brain. It has become one of the most influential topics today. It is a fundamental part of deep learning algorithms, which associates artificial intelligence.
So, you can understand the importance of artificial neural networks as it is somehow associated with artificial intelligence, the most discussed topic of today. We are presenting this article to make you understand all those things that you should know regarding artificial neural networks. So let’s start it.
What is an Artificial Neural Network?
In the beginning, we should clear the definition of artificial neural networks. An artificial neural network is a piece of a computing system that works as an animal brain. An artificial neural network can solve problems that may be impossible to solve by human or statistical standards.
Similarities Between Artificial and Biological Neural Network
We have mentioned earlier that the human brain inspires the artificial neural network. It replicates the function of the human brain. The biological brain is consists of approximately 100 billion neurons which are the basic processing unit of the brain.
Here in the case of artificial intelligence, artificial neural networks also perform the same thing the artificial intelligence unit. So, artificial neural networks are the neuron of artificial intelligence. The learning process is also the same for both the neural networks.
Structure of ANN
The basic structural component of an artificial neural network is artificial neurons that are arranged into layers. The three functional layers are
- One input layer
- One or multiple intermediate layer(s)
- One output layer
The input layer collects data from outside sources. This layer contains multiple neurons, and each neuron has different weights. When the neurons of the input layer receive data, they pass that data to the hidden layers for processing.
Hidden layers are the most important layers of the network system that remain in between the input and output layers. Hidden layers process and compute the data.
Neurons of these layers apply different transformations to the input data. Synapses are the connections of the neurons of different layers. All the hidden layers strongly connect through the synapse.
The output layer is the last layer of the neural network system. It receives data from the last output layer. With this layer, we can get the desired number of values in the desired range.
Most of the neural networks contain these three layers. Neurons of each layer are interconnected with each other strengths. It also connects the neurons of other layers.
How Artificial Neural Network Works?
The function of an artificial neural network is the combined function of all of its neurons. The total process is quite complicated, but we will make you understand very simply and briefly. The neurons of the input layer collect information from outside sources like data files, images, hardware sensors, microphones, etc.
The input layer also passes the data to the intermediate hidden layers quickly. The input layer receives the data in the form of a pattern or image, but they transfer to the hidden layer in the form of vectors.
Those vectors are calculated in intermediate hidden layers, and the result of the calculation is passed to the output layer. Output layers show the output as a result.
Training of Artificial Neural Networks
Training is a crucial part that decides the function of artificial neural networks. The key to performing the task with ANN is to adjust the weights of the connections of neurons. Training is compulsory for ANN to find the right weights.
For example, if you want to train an image classifier, you have to provide it multiple photos, labeled with its corresponding class (person, vehicle, animal, tree, etc.). At the time of training, the neural network adjusts itself to glean with the specific patters from the data.
Each layer of the hidden layers detects a specific class of data from the input. It processes the data and passes it to the next layer. Thus every layer analyses some of the input and passes the result to the next one.
The deeper layers can process more complex data than the superficial layers. Thus the artificial network developers train the neural network.
Applications of ANN
Following are some of the applications of artificial neural networks:
Text Classification and Categorization: Text classification is necessary for web searching, information filtering, language identification, readability assessment, and sentiment analysis. Most of the developers prefer to use a neural network for these purposes.
Named Entity Recognition: The aim of named entity recognition is to classify named entities like persons, locations, organizations, time, date, and so on. If you give some names like Sachin Tendulkar, India, Microsoft, etc., the artificial neural networks will classify them into predefined categories.
Speech Recognition: Speech recognition has a massive use in home automation, mobile telephony, virtual assistance, hands-free computing, and video games.
Character Recognition: Character recognition systems also have numerous use like receipt character recognition, legal billing document character recognition, check character recognition, etc. artificial neural network is used to get character recognition accuracy.
Spell Checking: Neural network is the finest tool for a text editor. Most of the text editors have an artificial neural network to check the spelling. Some spell-checking tools are also using neural networks.
We have included all of those things which you should learn about artificial neural networks. Though the artificial neural network is one of the fastest-growing technologies, it still has some limits. You need a lot of data to train an artificial neural network, whereas the human brain does not need so much data.
However, developers are working on it to make it more accurate. We can surely expect some more advanced artificial neural networks in the future.