The abstract theory defines a neural network architecture composed of distinct computational layers (input, hidden, and output) that maps input feature vectors to target outputs through non-linear transformations. This structure utilizes the formal mathematical construct of multilayer feedforward graphs where nodes in each layer represent stochastic units or neurons with associated weight matrices and bias terms governing signal propagation. As a foundational subfield within connectionist modeling, this architecture establishes the necessary structural prerequisites for gradient-based learning algorithms by defining differentiable loss landscapes over parameter spaces.
Introduces neural networks with handwritten digit recognition: neurons as activation holders, layers connected by weights and biases, and how the layered structure turns pixels into predictions.