How do deep neural networks learn high-level features from input data without human guidance?

Deep neural networks learn high-level features from input data by using multiple stacked layers that transform the input through nonlinear functions. Each layer progressively extracts more abstract features, allowing the network to identify complex patterns without human guidance. This process is driven by training the network to minimize prediction errors using backpropagation, which adjusts the weights of the connections between layers.

Deep neural networks are composed of multiple layers, each containing units that apply nonlinear transformations to their inputs. The network learns to extract high-level features by stacking these layers, where each subsequent layer builds on the features learned by the previous one. During training, the network adjusts its weights through backpropagation, which involves comparing the network's predictions to the actual outcomes and propagating the error back through the network to update the weights. This iterative process allows the network to autonomously learn complex features from unstructured data, such as images or text, without requiring explicit feature engineering by humans.