AI is a technology that is developing really fast. No wonder it became so popular. One of its advances is artificial neural networks, which are inspired by the structure of the human brain. It is thanks to them that machines and computers can look more like people. The 21st century is a period of rapid development of neural networks. They are commonly used by machine-learned algorithms, and these, in turn, can be found today not only in computers but also in many other electronic devices. Discover AI consulting to find out more.
What are artificial neural networks?
Neural networks, also known as artificial neural networks (ANNs), are part of ML and form the basis of deep learning algorithms. Their name and structure are based on the human brain and ‘mimic’ the biological neurons’ communication.
How do neural networks work?
As mentioned earlier, NN models consist of three layers of neurons: the input layer, hidden layers, and output layer. Each of them has a different task.
- Input layer: its task is to retrieve input parameters. It usually consists of several neurons equal to the number of input parameters.
- Hidden Layers: There may be more than one hidden layer in a given network. It is responsible for learning and performing calculations.
- Output layer: the layer that returns the result of the operation.
In short, an artificial neuron collects input signals, converts them, and sends an output signal. These layers are connected by nodes, and these connections form a “network” – a neural network – of interconnected nodes. Let’s illustrate it with an example. Suppose you want to recognize some 50×50 pixel image. So, in the input layer, there should be 2,500 neurons. These values go to hidden layers, where calculations take place. The calculated response is in the output layer.
Types of neural networks
FEED-FORWARD NEURAL NETWORKS
The feed-forward neural network is a model of a neural network that passes data only in a single direction. The information moves from the input layer through any hidden layers to the output one. Although this model is also called the multilayer perceptrons model (MLP), it uses sigmoidal neurons. Thanks to this, it is possible to solve real problems. To train the feed-forward neural network, you need data. These types of neural networks are the foundation of computer vision, facial recognition, natural language processing, and other neural network models.
RECURRENT NEURAL NETWORKS
Recursive neural networks (RNN) are distinguished based on feedback loops. The data received at the network output goes back to its input. This increases the predictive power of the network. What distinguishes this model from the others is primarily the ability to remember and use all the data again. These learning algorithms are mainly used in time series applications and future forecasting. So, they can predict stock prices, analyze sentiment, or forecast sales.
CONVOLUTIONAL NEURAL NETWORKS
Convolutional neural networks (CNNs) are highly popular neural network models. They typically consist of three types of layers: a convolutional layer, a buffer layer, and a fully connected layer. Each layer serves a different role, performs a task on the acquired data, and learns more and more complicated things. How do CNNs work? CNNs acquire and process large amounts of data in a grid format. Next, they extract relevant details for classification and detection. In addition, CNNs identify patterns in an image using the principles of linear algebra, in particular matrix multiplication. Hence, this model is used for image recognition, pattern recognition, or computer vision.
MODULAR NEURAL NETWORKS
Modular neural networks consist of a series of independent networks. An intermediary supervises their operation. In an independent network, each input is used to accomplish part of the overall objective of the larger network. During computation, the modules do not exchange information or interfere with one another. With this complex computing, processes are faster and more efficient.
The role of Neural Networks in AI
Many organizations use AI technology to solve different problems. Here are some uses of NN:
- Classification: It can be the classification of objects, text, and speech. Thus, NN can help, for example, in managing large databases.
- Recognition: It can be the recognition of objects or faces. This also finds its application in many cases, for example, facial recognition by cameras.
- Forecasting: That is, for example, creating a simulation of the development of events based on the provided data.
Creation. With NN, you can create images from sketches.
Artificial intelligence encompasses many technologies, including neural networks. In essence, neural networks mirror the behavior of the human brain. They enable computer programs to recognize patterns and solve common problems in the fields of AI, ML, and DL. Artificial neural networks find their applications in many areas. We can distinguish between facial recognition, classification, automatic transcription, forecasting, and so on. Therefore, this is another element of the 21st-century technical revolution, which can greatly facilitate our lives in many different aspects.