For humans, the use of neural networks opens up opportunities to work with gigantic arrays of data.
Different types of neural networks are used depending on the tasks they solve. The main types are perceptrons and multilayer networks, recurrent and convolutional models.
Simple perceptron
A perceptron is a basic neural network model that was first implemented in 1960. It consists of a single neuron that receives input, applies an activation function, and produces a binary output. This type of network australia phone number list is suitable for simple tasks where objects need to be classified into two classes, such as yes or no. However, due to the limitations of the single-layer perceptron, it is rarely used in modern systems.
Multilayer perceptron
The multilayer perceptron (MLP) appeared in 1986 and consists of several layers of neurons: input, hidden, and output. It uses nonlinear activation functions, which allows it to solve more complex problems, such as speech recognition or image editing. This architecture is also used to solve a wide range of problems, including sales forecasting or text analysis.
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Recurrent Neural Networks (RNN)
RNNs were also created in 1986 and are used when the context of the data is important. They use cycles to store information about previous steps. This helps in tasks related to time series analysis, such as forecasting, text generation, or speech recognition. RNNs are actively used in chatbots and automatic translation systems.