Neural networks have radically transformed the field of image analysis. Before delving into a practical example, it is important to understand what neural networks are. In simple terms, they are algorithms designed to recognize patterns, inspired by the workings of the human brain. These networks can learn complex tasks, such as recognizing and classifying objects within an image.
Basic Functioning of Neural Networks
A neural network is composed of layers of nodes, where each node represents an artificial neuron. The basic structure includes an input layer, hidden layers, and an output layer. Through a process known as training, the network adjusts the weights that connect the neurons to improve the accuracy of the final result.
Comparative Analysis: Use in Image Detection
| Criteria | Traditional Methods | Neural Networks |
|---|---|---|
| Efficiency | Low in nonlinear tasks | High due to deep learning |
| Precision | Limited by rigid programming | > Improveable with more data and training |
| Adaptability | Poorly adaptable to new situations | Highly adaptable to continuous learning |
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