To explain it very simply — neural network - is the way in which the computer learns to recognize patterns, as the human brain does.
It consists of many "neurons" - small computing nodes that process data, make decisions and pass on the results.
Real-life example: when you show a neural network thousands of photos of cats and dogs, it gradually "understands" which features are characteristic of a cat (ears, muzzle, whiskers) and which are characteristic of a dog (shape of the muzzle, ears, body). After training, it will be able to say with confidence: "Yes, this is a cat!" - even if it has not seen this particular photo before.
How the training works
Training a neural network can be compared to how a person learns to ride a bicycle — through errors and feedback.
Data entry — the network receives input data (for example, a picture).
Prediction — she tries to guess the result: who is in the photo — a cat or a dog.
Error - compares his answer with the correct one and determines the degree of error.
Weight adjustment - changes the connections between "neurons" to improve the result.
After thousands of iterations of such attempts and corrections, the network becomes more and more accurate — like a student who solves a problem hundreds of times until he memorizes the algorithm.
The architecture of the machine's brain.
To understand how neural networks "think", imagine them as layered cake:
Input layer — Receives data (for example, image pixels).
Hidden layers — perform complex transformations and find patterns.
Output layer — gives the result: for example, "0.9 — cat, 0.1 — dog".
Each layer is trained to look for increasingly complex features: the first - lines and colors, the second - the shape of the ears, the third - the whole face of the cat.
Why have neural networks become so powerful?
The reason for success is simple:
Lots of data — the Internet has provided billions of examples for learning.
Powerful processors (GPU) - accelerate the math under the hood.
New architectures — for example, transformers (ChatGPT works on them).
Where are neural networks used?
Today they are around us:
Voice assistants (Siri, Alice, ChatGPT)
Instagram filters
Autopilot in cars
Medicine - tumor recognition
Translators and chatbots
And even when Netflix recommends a TV show to you, it's also a neural network under the hood 😉
If you want to figure out how to train such neural networks in Python, come to the application Code - everything is explained in simple words, with practice and interactive lessons.
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Neural networks is not magic, but mathematics, supported by powerful calculations and big data. They learn the same way we do — through mistakes, observations, and experience. And the more data we give them, the smarter they become.