Neural networks are mathematical models inspired by the structure and function of the human brain, used in computer science and artificial intelligence. They consist of interconnected nodes called neurons, which are organized into layers: an input layer, hidden layers, and an output layer.
Each neuron receives signals (input), processes them through an activation function, and passes the result to the next layer. The connections between neurons have weights, which are adjusted during training to improve the network's accuracy.
Neural networks are particularly well-suited for tasks like pattern recognition, natural language processing, and image recognition, as they can learn to identify complex relationships in large datasets.
gRPC
