Artificial neural network: what is it and what is it for?

An artificial neural network or Neural Network is a computer system that uses the human brain to learn. Find out everything you need to know about this artificial intelligence technology from the Deep Learning family.

In 2019, artificial intelligence is in the spotlight. This technology is developing at high speed, and its use cases are multiplying in all sectors. According to experts, AI is going to change every aspect of our society. in the years to come.

In this context, it seems important to understand how artificial intelligence actually works. That’s why today we offer you to learn more about the artificial neural networks on which many AIs are based.

What is an artificial neural network?

An Artificial Neural Network is a hardware and/or software computer system. whose functioning is modelled on that of the neurons of the human brain.

This is a variety of Deep Learning technology, which is itself part of the artificial intelligence subcategory of Machine Learning.

The history of artificial neural networks

The concept of artificial neural networks was invented in 1943 by two researchers at the University of Chicago… neurophysicist Warren McCullough, and mathematician Walter Pitts. In an article published in the journal Brain Theory, the two researchers present their theory that the activation of neurons is the basic unit of brain activity.

In 1957, the Perceptron was invented. It is the oldest Machine Learning algorithmThe new “PATH” tool, designed to perform complex pattern recognition tasks. It is this algorithm that will later allow machines to learn to recognize objects on images.

Unfortunately, at the time, neural networks were limited by technical resources. For example, the computers weren’t powerful enough to process the data necessary for the operation of neural networks. This is why research in the field of Neural Networks has been dormant for many years.

We had to wait until the early 2010’s, with the rise of Big Data and massively parallel processing…to provide Data Scientists with the data and computing power to run complex neural networks. In 2012, in a competition organized by ImageNet, a Neural Network succeeded for the first time in surpassing a human in image recognition.

That’s the reason whyhis technology is once again at the heart of the scientific community’s concerns.. Nowadays, artificial neural networks are constantly improving and evolving day by day.

How does the artificial neural network work?

neural network functioning

As a rule, a neural network is based on a large number of processors operating in parallel and organized as third parties. The first third party receives the raw information inputs, much like the optic nerves of a human being… when processing visual signals.

Afterwards, each third party receives information outputs from the previous third party. We find the same process in humans, when neurons receive signals from neurons close to the optic nerve. The last third, on the other hand, produces the results of the system.

How does the artificial neural network learn?

By means of an algorithm, the artificial neural network allows the computer to to learn from new data. The computer with the neural network learns how to perform a task by analyzing examples to practice. These examples have been labeled beforehand so that the network can know what it is about.

For example, a neural network can be used to teach the computer to recognize objects. A large number of objects of the same category are presented to the neural network, and the computer learns to recognize this object in new images by analyzing the recurring patterns within the example images. For example, by analyzing thousands of photos of cats, the Neural Network will learn to recognize a cat in any given photo.

In contrast to other types of algorithms, neural networks cannot be programmed directly to perform a task. Like a child’s developing brain, the only instruction they have is to learn.

However, there are three distinct learning methods. In the case of supervised learningthe algorithm trains on a set of labelled data and modifies itself until it is able to process the dataset to obtain the desired result.

In the case of unsupervised learningthe data is not tagged. The neural network analyzes the data set, and a cost function tells it how far it is from the desired result. The network then adapts to increase the accuracy of the algorithm.

Finally, with the enhanced learningThe neural network is strengthened for positive results and sanctioned for negative results. This is what allows it to learn over time, in the same way that a human gradually learns from his mistakes.

What are the different types of networks at Neural Networks?

We can distinguish different types of neural networks. Generally, Neural Networks are categorized based on the number of layers that separate data input from output, the number of hidden nodes in the model, or the number of inputs and outputs of each node.

Depending on the type of network, the propagation of information between different third parties of neurons can vary. In the simplest variant, the feed-forward neural network. If you use the “Input” option, the information goes directly from the input to the processing nodes and then to the outputs.

The recurrent neural networksIn turn, they save the results produced by the processing nodes and feed the model with these results. This learning mode is a bit more complex.

Finally, the convolutional neural networks are more and more used in different fields: facal recognition, text digitization, natural language processing…

What are the usual cases of use?

So, concretely, what are artificial neural networks used for? Well… their cases of use are numerous, and are multiplying over time. Examples of applications include handwriting recognition, speech-to-text transcription, or financial market forecasting (also called “algorithmic trading”).

Neural Networks can also be used for facial recognition, weather prediction, cancer detection in medical imaging, and predictive analytics in business. They are also used to train chatbots, natural language processing algorithms, or to discover new drugs. In general, artificial neural networks are excellent for pattern recognition, complex signal processing and prediction.

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