NEURAL-NETWORKS

NEURAL-NETWORKS are computational models within the field of ARTIFICIAL-INTELLIGENCE that are designed to simulate the way the human brain processes information. These systems are the core components of DEEP-LEARNING, consisting of layers of artificial neurons that transmit signals to one another. As described by IBM, each connection between neurons has an associated weight that is adjusted during the training process to improve the model's predictive accuracy.

The architectural design of NEURAL-NETWORKS typically includes an input layer, multiple hidden layers, and an output layer. Data processing involves passing signals through an ACTIVATION-FUNCTION to introduce non-linearity, allowing the network to learn complex patterns. The training phase relies heavily on BACKPROPAGATION and GRADIENT-DESCENT to minimize the loss function. Key figures in the development of these technologies include GEOFFREY-HINTON, YOSHUA-BENGIO, and YANN-LECUN, who are often credited with the modern resurgence of the field, according to resources like Wikipedia.

Today, NEURAL-NETWORKS power a wide range of technologies, from NATURAL-LANGUAGE-PROCESSING to autonomous vehicles. Scientific journals such as Nature have documented the significant impact of these models on various domains of research and industry.

• MACHINE-LEARNING
• DEEP-LEARNING
• ARTIFICIAL-INTELLIGENCE
• REINFORCEMENT-LEARNING