Deep learning is a subclass of machine learning defined by neural networks with three or more layers, where each layer learns a progressively higher-level abstraction of the data. Its parameters are the weights and biases of the neural network, persisted through trained model checkpoints, framework implementations (TensorFlow, PyTorch), and the academic literature that specifies architectures (CNN, RNN, transformer). The mechanism of persistence is the reproducible training pipeline: architecture specification + data + optimizer + computed weights, all maintained in code and weights repositories. [formal: doctrina profunda | substrate: mind | horizon: generations | explicit: yes | epoch: 0.01]
Accepted ontology entry
deep learning
Deep learning is a subclass of machine learning defined by neural networks with three or more layers, where each layer learns a progressively higher-level abstraction of the data. Its parameters are the weights and biases of the neural net…
Definition
Why it is in scope
A human-constructed approach to machine learning that uses artificial neural networks with multiple hierarchical layers to learn increasingly abstract representations from data. Built through engineered architectures (CNNs, RNNs, transformers) and optimized via backpropagation across layered weight matrices.
Names and aliases
- deep learningen · CANONICAL
Relations from this entry
- cmrg0scos00ef2a1nklfvbk7xINSTANCE_OF →
Deep learning is a specific kind of machine learning — a competent ML practitioner would call it 'a kind of ML.' The defining feature is multi-layer neural network architectures. Nearest kind: machine learning (Law 9).
- cmrg0scos00ef2a1nklfvbk7xDERIVED_FROM →
Machine learning existed first and fed into deep learning. Deep learning is a specialized subset of ML using multi-layer neural networks. Chronology test: ML predates DL by decades.
Relations to this entry
- cmsn3fj8r02vj1q1387edzpxt← SERVES
Self-attention was designed and is maintained for the sake of deep learning — its purpose is to enable parallelizable sequence processing within deep neural networks, furthering deep learning's capabilities. Per Law 8d (SERVES): the servant (self-attention) points at the master (deep learning). Its designed purpose is to further deep learning's operation.
Record identity
- Created
- Aug 10, 2026, 4:26 AM UTC
- Content hash
- ad98099fc598434df7da89209f4165d0d4247b40094db343a1a16a3174feea4a