An artificial neural network is a computer architecture inspired by the human brain, where artificial “neurons” are interconnected to process information. These networks allow machines to learn from data by adjusting the weights of connections between neurons, which helps improve performance over time.
Structure of a neural network
Neural networks are made up of several layers of neurons:
- Input layer : It receives the raw data (images, text, audio, etc.).
- Hidden layers : These are the layers where most of the data processing takes place. Neurons in the hidden layers apply mathematical transformations to the input data.
- Output layer : It provides the final result, such as a classification or prediction.
Each connection between neurons has a weight that is adjusted during training to improve the accuracy of the network.
Types of Neural Networks
- Multilayer Perceptron (MLP) : The simplest type of neural network. It consists of multiple layers of neurons, but each neuron is connected only to neurons in the next layer.
- Convolutional Neural Networks (CNN) : Widely used for image analysis, these networks use convolutions to extract important features in an image (edges, textures, shapes).
- Recurrent Neural Networks (RNN) : Suitable for processing sequential data, such as text or time series, because they can “remember” previous information through internal loops.
- Generative Adversarial Networks (GANs) : These are networks where two neural networks compete: a generator that creates data and a discriminator that tries to distinguish the generated data from real data. They are very popular for creating realistic images, videos, or sounds.
Training Neural Networks
Training a neural network involves adjusting the weights of the connections between neurons to minimize the error between the predicted output and the expected output. This process is called backpropagation , where the error is propagated back through the network to adjust the weights.
Applications of neural networks
Neural networks are widely used in various fields:
- Speech recognition and natural language processing : Virtual assistants like Google Assistant or Alexa use neural networks to understand and answer questions in natural language.
- Computer vision : Convolutional networks are used in facial recognition systems, medical image analysis, or even in autonomous vehicles to analyze the environment.
- Content Creation : GANs are used to create artificial artwork, videos, or music.
Neural networks are one of the cornerstones of modern artificial intelligence, enabling complex systems to learn and perform tasks that were once impossible.