Neural network

A neural network is a type of artificial intelligence design inspired by how the human brain works. It is like a network of interconnected nodes that learns to recognize patterns and solve problems by processing information through different layers.

Imagine an AI model as a factory with its work system (neural network) composed of several departments (layers). In each department, there are multiple workers (nodes) specialized in different aspects of the same task. Information passes through these departments, being processed increasingly complexly, according to the rules (parameters ) that the work system has learned from its previous experience (training ).

For example, in a neural network that recognizes dog images, the first department has workers specialized in detecting basic edges, lines, and curves. The next department has other workers who combine these characteristics to identify more complex shapes like eyes or paws. The final department has workers who analyze all these characteristics together to determine whether the image is a dog or not.
During training , the network adjusts its parameters , gradually improving its precision in the task. This allows the network to recognize dogs in different contexts, sizes, and positions, not just those with which it was initially trained .

Neural networks are organized into different specialized types, such as Convolutional (CNN) for image processing and Recurrent (RNN) for sequential data. These combine in different ways to create more complex architectures like Transformers , the foundation of virtually all modern language models (GPT, Claude, Gemini, Llama, etc.), or Diffusion Models , dominant in image generation (Stable Diffusion, DALL-E, Midjourney).

Neural network

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