Neural network — is a program that makes decisions by imitating the human brain. It analyzes data, evaluates options, and draws conclusions. This approach allows solving tasks that were previously considered intractable for computers.
Each neural network consists of three main components:
- Input layer — where data "enter" the system.
- Hidden layers — where information processing occurs.
- Output layer — where we get the result.
All nodes (neurons) in the layers are connected to each other. Each node has its own "weights" — indicators of importance, and an activation threshold. If the value exceeds the threshold, the neuron "turns on" and transmits data further. If not — the data remains in place.
To run a neural network, it is not enough to simply launch it. It needs to be trained — and this is where the most interesting part begins.
How do neural networks work?
Let me explain with a simple example. Imagine you are deciding whether to go for a bike ride today. There are three factors:
- Is the weather good? (Yes — 1, No — 0).
- Are there many cars on the road? (Yes — 0, No — 1).
- Do you have time? (Yes — 1, No — 0).
Let's say the weather is excellent (1), there are few cars on the road (1), and you are short on time (0). Now we assign a "weight" to each factor — its importance:
- Good weather is the most important (5).
- Few cars — this is also important, but a little less (4).
- Time plays a small role (2).
There is also a threshold value: 7. If the sum of the data is above the threshold — we decide to go. Let's calculate: 1 x 5 +1 x 4 + 0 x 2 = 9
Nine is greater than seven — so you go for a ride.
This example — is a simplified version of how neural networks work. They analyze many factors, assign weights to them and make a decision based on a threshold value.
Different types of neural networks
Neural networks are not all the same — it all depends on their tasks.
- Perceptron: the first version of a neural network, created in 1958.
- Feedforward neural networks: analyze text, images or speech.
- Convolutional neural networks (CNN): work with images — from face recognition to medical image analysis.
- Recurrent neural networks (RNN): forecast time series, such as sales or exchange rates.
A little history
- In 1943, the first theories about how the brain processes information appeared.
- In 1958, the perceptron was created — the first neural network.
- In 1974, the backpropagation algorithm was developed, which is still used to train neural networks.
- In 1989, it was possible to train a neural network to recognize handwritten digits — this became a revolution.
Why is this important?
Modern neural networks help solve tasks that were previously unavailable: speech recognition, image analysis, market behavior prediction. But behind the simplicity that we see from the outside lies a complex and labor-intensive process of creation, training and optimization.
That is why working with neural networks — is not just "taking a couple of photos and uploading them to the system". It is deep knowledge, experience and resources needed to create truly intelligent systems.