What is an artificial neural network?

Here's everything you need to know

If you spend any time reading about artificial intelligence, you will almost certainly hear about artificial neural networks. But what exactly is one? Instead of enrolling in a comprehensive computer science course or searching for some more in-depth resources available online, check out our handy Leperson Guide to get a quick and easy introduction to this amazing type of machine learning.

What is an artificial neural network?

Artificial neural networks are one of the main tools used in machine learning. As the "neural" part of their name suggests, they are brain-induced systems that are intended to replicate our human way of learning. Neural networks consist of input and output layers, as well as (in most cases) hidden layers that contain units that convert the input to what the output layer can use. These patterns are excellent tools for detection that are very complex or innumerable for human programmers to extract and teach to recognize machines.

While neural networks (also called "preceptors") have existed since the 1940s, it is only in the last several decades that they have become a major part of artificial intelligence. This is due to the advent of technology called "backpropagation" which allows the network to adjust the hidden layers of neurons in a situation where the result does not match what the creator had hoped for - like a network designed to recognize dogs, such as cats.

Another important advance is the advent of a deep learning neural network, in which different layers of a multilayer network extract different features until it can recognize what it is looking for.

Sounds very complicated. Can you explain it like I'm five?

For a basic idea of ​​how to learn deep neural networks, imagine a factory line. After the raw material (data set) input, they pass under the conveyor belt, extracting different sets of high-level features at each backstop or layer. If the network is intended to identify an object, the first layer can analyze the brightness of its pixels.

The next layer can identify any edges in the image, based on lines of the same pixels. After this, the next layer can identify textures and shapes, and so on. By the time you reach the fourth or fifth level, deep learning traps have created complex feature detectors. It can be found that some image elements (such as a pair of eyes, a nose, and a mouth) are normally found together.

Once this is done, investigators can label the output given the network training, and then use back properties to correct any errors. After a while, the network can perform its classification tasks without the help of a person each time.

In addition, there are different types of learning, such as supervised or untrained learning or reinforcement learning, in which the network goes out of its way to maximize its score - as commemorated by Google DPMind’s Atari game-play bot.

How many types of neural networks are there?

There are many types of neural networks, each with its own specific use cases and levels of complexity. The most basic type of neural net is called feedforward neural network which travels in only one direction from information input to output.

The most widely used iteration of the network is the neural network, in which data can flow in many directions. These neural networks have more learning potential and are widely used for more complex tasks such as learning handwriting or language recognition.

There are also Confusion Neural Networks, Boltzmann Machine Networks, Hopfield Networks, and many others. Choosing the right network for your job depends on the data you train and the specific application you have in mind. In some cases, it may be worth using multiple approaches, such as the case with challenging tasks such as voice recognition.

What kind of functions can a neural network perform?

A quick scan of our archives suggests that the appropriate question here should be "Can't the neural network do the work?" From making cars drive autonomously on the road, to producing amazing real CGI faces, translating machines, detecting fraud, reading our brains, recognizing that the cat is in the garden, and turning on sprinklers; Neural trap A.I. Is behind many great advances.

Roughly speaking, however, they are designed for sputtering patterns in data. Specific tasks may include classification (data set classification into predefined categories), clustering (classification of data into various undefined categories), and prediction (using to predict future events such as the stock market or film box office).

How exactly do they match the "stuff"?

In the same way that we learn from experience in our lives, neural networks need to learn data. In most cases, the more data that can be thrown into a neural network, the more accurate it will be. You think of no more. Over time, you will gradually become more efficient and make fewer mistakes.

When researchers or computer scientists go out to train neural networks, they usually divide their data into three sets. The first is a training set, which helps the network to establish different weights between its nodes. After that, they fined it using a validation data set. Finally, they will use the test set if it can successfully convert the input to the desired output.

Are there any limitations to the neural network?

At the technical level, a major challenge is the amount of time it takes for networks to train, which requires a sufficient amount of computing power for more complex tasks. The biggest issue, though, is that these neural networks are “black boxes” in which the user feeds on data and receives responses. They can fine-tune the answers, but they do not have access to the right decision-making process.

This is a problem that many researchers are actively working on, but it will only be more stressful when artificial neural networks play a bigger and bigger role in our lives.