What is an artificial neural network?

Artificial neural network (ANN) is the most common meaning of neural network. It is a complex series of interconnected artificial neurons that mimic those of the human brain and are used in artificial intelligence to process information, learn, and predict.

Neurons are the most basic cells of the human brain. The human brain has billions of neurons that interact and communicate with each other to form a neural network.

These neurons receive many inputs, from what we see and hear to what we feel and everything in between, and then send information to other neurons, who in turn respond. Effective neural networks enable humans to think and, more importantly, learn.

As a method of acquiring large amounts of data, processing it, and making predictions and decisions based on the data, the human brain's neural network is by far the most powerful computing power known to man.

Neural network is technically a biological term, while artificial neural network is the neural network upon which artificial intelligence relies.

Although the word itself is most commonly used to refer to artificial neural networks, you'll often see people refer to artificial neural networks simply as neural networks.

Of course, neural networks in the human brain are very different from artificially constructed neural networks. Still, the basic way they process information and make predictions remains the same.

While artificial neural networks will not be perfect recreations of biological neural networks, artificial neural networks are based on and modeled after the brain's neural networks precisely because of the computational power of these networks.

Humans use biological neural networks to process information, learn, and make predictions, such as thinking. Artificial neural networks work in much the same way, but to a lesser extent, because artificial neural networks cannot yet match the complexity and functionality found in the human brain.

Artificial neural networks achieve more complex, realistic, and powerful artificial intelligence through deep learning, which is the process by which artificial neural networks learn independently and make their own decisions.

Artificial Neural Network Example

Artificial neural networks are used in supermarket chains to scan products entering and exiting distribution centers, in self-driving cars to recognize road signs, in banks to control ATM networks, and in smartphones for pattern recognition (e.g. recognizing faces, objects). and fingerprints).

Human-like artificial intelligence is possible with advanced neural networks and enough data to train (or teach) the neural network. As shown in the movie, artificial intelligence does not exist today, but if it did, deep learning through neural networks would power this intelligence.

  • Also known as deep learning, it is a subfield of artificial intelligence machine learning that involves algorithms based on modeling of brain structure and function. Deep neural networks are designed to recognize digital patterns and convert them into real-world data, such as images, text, or audio.

  • It is a class of deep neural algorithms commonly used to analyze visual images. Convolutional neural networks receive images and use filters to extract features and are mainly used for image processing, classification and segmentation.

  • It is an artificial neural network commonly used in speech recognition and natural language processing. Recurrent neural networks use sequential or time series data to solve common timing problems in language translation and speech recognition.