What is a Neural Network in Simple Words?

What is a Neural Network in Simple Words?

The inevitable has happened. What seemed impossible just 50-70 years ago is now closely intertwined with humanity. We are, of course, talking about neural networks.

Let’s dive into this complex topic: what is a neural network, who needs it and why, how does it work, what makes it special (if it indeed has anything special), and what exactly is it used for? In other words, what role does it play in our lives?

To say that artificial intelligence has taken over not only the World Wide Web but is now ready to dominate everything living on the planet would be an understatement. Everyone knows about neural networks, from young schoolchildren to respected elders across the globe. And yes, we are all using them in our daily lives without even thinking. Smart homes, self-driving cars, and the gadgets in our hands — all of these are powered by neural networks.

There are even more areas where neural networks are indispensable. Without them, we can’t predict the weather, determine stock market movements, process speech, write thick books, or enhance our photos.

Definition of a Neural Network

In programming terms, a neural network is a special computational model. It consists of artificial neurons grouped into "nodes," much like the neurons in the human brain. These artificial neurons are organized into specific levels or layers.

Individually, each neuron doesn’t do anything special; they simply perform basic mathematical operations — yes, just simple additions or multiplications.

Within these layers, the neurons work together so that each one performs the simplest part of a larger task and passes it on. Ultimately, through the combined work of all artificial neurons, the result is generated — an image, a text, or even a melody.

What Types of Neural Networks Are There?

Neural networks come in several types, each capable of performing different tasks.

The simplest ones are single-layer perceptrons. These can solve the simplest problems.

For more complex tasks, there are multi-layer perceptrons (MLP), which can have more than one layer, referred to as “hidden” layers.

For recognizing and processing visual inputs, there are convolutional neural networks (CNN).

Models capable of remembering previous states are called recurrent neural networks (RNN).

For generating new data that looks like real-world data, there are generative adversarial networks (GANs), which are more advanced and consist of two networks — a generator and a discriminator.

How Do Neural Networks Work?

Any neural network consists of an input layer, hidden layers (which can actually be many), and an output layer.

Each of these layers has a specific job. The input layer processes the incoming data so it can be passed on to other layers for further action.

The main calculations take place in the hidden layers. In each of them, signals are processed by neurons, multiplied by their weights, and after summation, a nonlinear activation function is applied. The more layers a neural network has, the more it learns and remembers.

The output layer, after collecting and processing the results from the previous two layers, gives the final answer.

Activation Functions and Weights

Sometimes, the input signal greatly influences the output, potentially leading to incorrect results. To understand and properly assess the degree of this influence, neural networks use weights (importance) or coefficients.

To help neural networks solve complex tasks, such as image recognition or text understanding, activation functions are used.

These are necessary to introduce nonlinearity into the neural network, enabling it to solve complicated problems like recognizing patterns or understanding texts.

The most popular ones are Sigmoid, Tanh (Hyperbolic Tangent), and ReLU (Rectified Linear Unit).

How Does Neural Network Training Work?

Machine learning is essentially the same as any other kind of learning. Imagine a child learning about the world by observing it. A neural network behaves almost the same way. It is shown thousands, even millions, of examples: “Look at this! It’s a cat!” And the neural network starts to notice: “Ah, a cat has ears, whiskers, and a tail.” They show it another image, and it thinks, “Yes, that looks like a cat.”

Did it make a mistake? Is it not a cat? No problem! The neural network learns from its mistakes! Just like a child who falls and gets up again, the neural network adjusts its internal connections.

This is called error backpropagation. The network looks at how far it was from the correct answer and adjusts its neurons to provide a closer answer next time.

Neural Network Learning Methods

There are two learning methods for neural networks. We all learn through examples and algorithms in various systems.

We learn at school, college, or university, with a teacher. Or we learn by ourselves, from books, textbooks, or online. The neural network also learns, either with or without a teacher.

It is loaded with various data, processes it, classifies, calibrates, evaluates, and at some point, there is a transition from quantity to quality — the neural network begins to “think.”

To understand its current stage, it is given a task. A prompt is introduced, and the neural network searches for the answer — not just any answer, but the correct one!

If the neural network is learning “with a teacher,” the correct answer is already in the data. The network just needs to find it, following the proper algorithm. The success of the search depends on how much information it has already learned. The more it has learned, the closer the answer will be to correct.

When deep learning occurs “without a teacher,” the correct answer is unknown in advance. The neural network must think carefully. It needs to process massive amounts of information, apply various methods, and build patterns to find the right answer.

Interestingly, in the second method of learning, the neural network often finds the answer in a way that’s even more original than expected. This is because it has to actively “work out” the solution on its own.

Applications of Neural Networks

The fields and areas of artificial intelligence applications are numerous. Let’s highlight the main ones.

Computer Vision

There are certain areas of human activity where high-quality visual analysis is required. For these tasks, convolutional neural networks (CNN) are used. They are applied to recognize various objects, faces, texts, and other visual data.

One example is self-driving cars, powered by artificial intelligence. These vehicles don’t just move; they think. They analyze traffic situations, predict the behavior of other drivers, and make decisions in fractions of a second. These are robots on wheels, armed with knowledge gathered from trillions of traffic scenarios.

Neural Networks in Medicine

Ah, this is an even more exciting field!

Neural networks can process vast amounts of data very quickly. They help detect serious, incurable diseases at the earliest stages.

Imagine how these algorithms can spot microscopic changes in tissues and find anomalies that even an experienced doctor might miss. The human factor is excluded, and the likelihood of errors is minimized.

Now, patients don’t need to seek second or third opinions to rule out incorrect diagnoses by doctors, traveling from city to city, clinic to clinic. Neural networks can immediately and accurately determine the cause of the illness, make predictions, and recommend the necessary treatment. They offer hope and save lives!

Texts, Texts, Texts...

The world is full of all kinds of writings! Everyone writes, all the time, everywhere! Every day, so many texts are produced, it’s hard to even imagine! And someone has to write them. In enormous quantities, surrounded by letters up to the “maximum.” This is where neural networks are essential.

Recurrent neural networks (RNN) write texts, translate texts, develop chatbots, and do much more to help writers.

Images

One could endlessly admire the works of great (and not so great) artists. Walking through museums, sighing, and marveling.

However, neural networks like Midjourney don’t need to attend art academies, spend years, and invest huge resources to reach a level where their "works" become world-famous. They can create highly realistic and artistic images based on a given prompt in no time.

Importantly, these images can be used for commercial purposes, meaning they can be monetized.

Risks of Using Neural Networks

It’s important to note that, despite all the advantages of neural networks, there is one very crucial aspect that can’t be ignored.

Data! These immense volumes of data! Hundreds of millions, billions of lines of information, hidden from our eyes. Neural networks go there and absorb these data… and what happens to them next?

This question requires a careful approach. Privacy, security, and responsibility are the key principles on which the use of neural networks should be based.

Conclusion

In conclusion, we can confidently say that artificial neural networks are one of the most advanced and promising fields of technology today. They are very close to the human brain in terms of style and problem-solving methods.

And despite many shortcomings, errors, and limitations of neural networks, they have already taken, or will soon take, one of the most important places in everyday human life, making our world much more humane!