Neural networks for beginners: what they are and how they work

Today, artificial intelligence (AI) is actively changing our world, and one of its most important components is neural networks. These technologies are used in everyday life – from facial recognition on smartphones to personalized recommendations in streaming services. But what are neural networks and how do they work? If you’re new to the field, this article will help you understand the basics and how to train a neural network to solve real-world problems.

What are neural networks

Neural networks are mathematical models inspired by the workings of the human brain. They are made up of nodes (“neurons”) that are connected to each other and are capable of transmitting information. Each neuron receives data, processes it and passes it on. This approach allows the model to adapt to change and solve complex problems.

 

Comparison with the human brain:

As in the brain, in neural networks, information is transferred through connections between neurons. In the brain, these connections are called synapses, and in neural networks they are called weights. If the weights are stronger, the signal is transmitted more efficiently.

Origin story:

The concept of neural networks emerged in the 1940s when Warren McCulloch and Walter Pitts proposed the first mathematical model of a neuron. In the 1980s, error back propagation algorithms emerged that made neural network training more efficient. A breakthrough occurred in the 2010s due to advances in computing and the availability of big data.

How neural networks work

What happens in a neural network?

Imagine a neural network as a group of people working together to solve a problem. Each person does their part of the work (neuron), and the result is passed on down the group (layers of the neural network). The end result is the response that the neural network generates, such as detecting an object in an image.

Major components:

• Neurons (nodes): Think of these as little “brain cells” that take in data, process it, and pass it on. For example, a single neuron might analyze the color of a pixel in a photo.
• Layers: Neurons are organized into layers:
• Input layer – accepts raw data (e.g., pixels in an image).
• Hidden layers – perform calculations by looking for patterns in the data (e.g., grouping pixels into lines or shapes).
• Output layer – generates a result (e.g., says there is a cat in the photo).
• Weights and activation function: Weights determine how important each input is. If the signal is strong enough (thanks to the activation function), it is passed on.

How does a neural network learn?

1. Data collection: For example, to recognize cat images, you need to have many pictures of cats and other animals.
2. Processing: The neural network analyzes each image and first makes a guess, which may be wrong.
3. Feedback: If the neural network is wrong, its settings (weights) are adjusted. This is done using an algorithm that minimizes the errors.
4. Repetition: The process is repeated many times until the neural network starts giving accurate results.

A simple example:

Let’s imagine that a neural network is learning to recognize the number “5”. First, it sees an image of the digit in the form of black and white pixels. The input layer accepts these pixels, the hidden layers analyze whether they have similarities with the number “5”. If the network is wrong (for example, decides it is a “6”), the weights are corrected, and the next time it analyzes it, it becomes more accurate.

Types of neural networks

• Artificial Neural Networks (ANN): Used for basic tasks such as text classification or number prediction.
• Converged neural networks (CNNs): Specialize in image analysis. They recognize faces, objects, and even pathologies in X-rays.
• Recurrent neural networks (RNNs): Process sequences of data such as text, music, or time series. For example, they can predict the next word in a sentence.
• Transformers: State-of-the-art models for text processing. They are at the heart of tools such as ChatGPT and Google Translate.

Where neural networks are used

• Image and video recognition: Neural networks analyze images in medicine (diagnostics), security (surveillance cameras) and marketing (consumer sentiment).
• Text analysis: Chatbots, automatic translation, emotion analysis in social networks.
• Autonomous driving: Neural networks help cars recognize objects on the road and make decisions in real time.
• Forecasting: Used in finance (market analysis), sports (predicting results) and weather.
• Gaming: Realistic characters and scenarios in games.

How to train a neural network

In order for a neural network to give more accurate answers, you need to follow a few simple rules:

1. use qualitative data

• Variety: Ensure that there are different examples in the dataset. For example, if you are training a neural network to recognize cats, use photos of different breeds, colors, and angles.
• Cleanliness: Remove unnecessary or incorrect examples from the data that could confuse the model.

2. train the model gradually

• Start with simple tasks. At first, the neural network can learn only to recognize if there is a cat in the photo, and then to determine its breed.
• Divide the data into three parts: for training, validation, and testing. This will help you evaluate how well the model works with the new data.

3. Check the quality of work

• After each training step, test the neural network. For example, show it photos it hasn’t seen before and check the results.
• If the model is often wrong, find the reasons. Perhaps there are not enough examples of a certain type in the dataset.

4. Improve the model

• Add more data. For example, if the neural network confuses gray cats with dogs, add more photos of such cats.
• Use modern techniques such as data augmentation to create more variations of the same example (e.g., rotate or darken the image).

5. Train the neural network without overloading it too much

• Don’t let the model memorize data. It should learn to understand general patterns, not just repeat what it sees.
• To do this, use techniques that prevent overlearning, such as Dropout (randomly turning off part of the neurons during training).

6. Constantly update the model

• Neural networks work better if they are regularly updated with new data. For example, if you add new categories, make sure the model
• knows about them.

Conclusion

Neural networks are a powerful tool that opens up many opportunities in a variety of fields. Even if you are just starting your journey, you can already find simple tools and resources to learn the basic principles of working with them. As you deepen your knowledge, you’ll not only understand how artificial intelligence works, but you’ll be able to apply it to real-world problems. Mastering this technology can be your first step towards an innovative future.