What is a neural network in simple terms
Short answer: A neural network is a machine that takes your question and gives an answer in text. But if you attach tools to it — code, API, databases — it can not just chat, but write programs, generate images, analyze tables. Imagine a library, only with computing power instead of books.
How a neural network works — in simple terms
You go into a library and ask: "How many planets are in the solar system?" The librarian opens one book — answer: 8. That's a regular program. Now imagine a library with millions of books, and the librarian has read them all. You ask: "Why did Pluto stop being a planet?" — and the librarian combines information from an astronomy textbook, a Wikipedia article, and a scientific journal. That's a neural network.
A neural network doesn't "think" like a human. It predicts the next word based on what it saw during training. So it can write Pushkin's poetry, Python code, and a microwave instruction manual — it all depends on what data it was trained on.
Why this is not "magic" but mathematics
Inside a neural network are layers of artificial neurons. Each neuron receives a number at the input, multiplies it by a weight, sums it up, and passes it on. Billions of such operations per second. When you type "Hello, how", the neural network calculates the probability of the next word. "Are" — 87%, "life" — 5%, "are you" — 3%. It chooses the most probable one — and there you have a meaningful answer.
This is both its strength and weakness. A neural network doesn't know facts — it knows probabilities. So sometimes it gives plausible but incorrect information. This is called a "hallucination".
What types of neural networks exist
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