1. What’s neural network?
A neural network is a computational model inspired by the functioning of the human brain, designed to perform specific learning and pattern recognition tasks. It is made up of interconnected nodes called neurons, organized in layers. Each neuron performs calculations based on weighted inputs, and the results are transmitted through connections to subsequent layers. These are used in the field of artificial intelligence and deep learning to perform tasks such as image recognition, natural language processing, time series analysis, development of Large Language Models, etc.
2. What’s the neural network function?
Neural networks, in the context of deep learning, are invaluable due to their ability to perform complex learning and pattern recognition tasks. He excels at problem solving in areas such as image recognition, natural language processing, and decision making. Its interconnected architecture of nodes, or neurons, allows it to learn and adapt by adjusting weights on the connections during training. This enables the network to capture subtle patterns and relationships in the data, allowing its application in a wide range of fields, such as face, license plate or object recognition in security systems using neural networks specialized in artificial vision.
A common example of neural networks consists of using them to analyze a set of historical data as time series, so their application in the financial system is notable. Convolutional neural networks are also good at detecting patterns in images, making them ideal for image classification tasks.
3. Example of neural networks.
- Generative neural networks.
- Recurrent neural networks.
- Convolutional neural networks
- Pretrained neural networks.
- Generative Adversarial neural networks.