👉 Connection math, also known as neural network math, is the mathematical framework that underpins the operation of artificial neural networks. It involves the use of linear algebra and calculus to define how neurons (or nodes) in a network process and transmit information. Each connection between neurons has a weight, which is adjusted during training to minimize error in predictions. The input to a neuron is multiplied by these weights, summed, and then passed through an activation function to produce the neuron's output. This process is repeated for each layer in the network, with the final output being a function of all previous layers' inputs and weights. The goal is to find optimal weights and biases that allow the network to accurately model complex patterns in data, effectively bridging the gap between abstract mathematical concepts and practical machine learning applications.
