# 3.4.4 deep learning, ISO 22989 Published contents Contents1 Scope2 Normative references3 Terms and definitions4 Abbreviated terms5 AI conceptsClause 3, terms 3.1 Terms related to AI3.1.1 AI agent3.1.2 AI component3.1.3 artificial intelligence3.1.4 artificial intelligence system3.1.5 autonomy3.1.6 application specific integrated circuit3.1.7 automatic3.1.8 cognitive computing3.1.9 continuous learning3.1.10 connectionism3.1.11 data mining3.1.12 declarative knowledge3.1.13 expert system3.1.14 general AI3.1.15 genetic algorithm3.1.16 heteronomy3.1.17 inference3.1.18 internet of things3.1.19 IoT device3.1.20 IoT system3.1.21 knowledge3.1.22 life cycle3.1.23 model3.1.24 narrow AI3.1.25 performance3.1.26 planning3.1.27 prediction3.1.28 procedural knowledge3.1.29 robot3.1.30 robotics3.1.31 semantic computing3.1.32 soft computing3.1.33 symbolic AI3.1.34 subsymbolic AI3.1.35 task3.2 Terms related to data3.2.1 data annotation3.2.2 data quality checking3.2.3 data augmentation3.2.4 data sampling3.2.5 dataset3.2.6 exploratory data analysis3.2.7 ground truth3.2.8 imputation3.2.9 input data3.2.10 label3.2.11 personally identifiable information3.2.12 production data3.2.13 sample3.2.14 test data3.2.15 validation data3.3 Terms related to machine learning3.3.1 Bayesian network3.3.2 decision tree3.3.3 human-machine teaming3.3.4 hyperparameter3.3.5 machine learning3.3.6 machine learning algorithm3.3.7 machine learning model3.3.8 parameter3.3.9 reinforcement learning3.3.10 retraining3.3.11 semi-supervised machine learning3.3.12 supervised machine learning3.3.13 support vector machine3.3.14 trained model3.3.15 training3.3.16 training data3.3.17 unsupervised machine learning3.4 Terms related to neural networks3.4.1 activation function3.4.2 convolutional neural network3.4.3 convolution3.4.4 deep learning3.4.5 exploding gradient3.4.6 feed forward neural network3.4.7 long short-term memory3.4.8 neural network3.4.9 neuron3.4.10 recurrent neural network3.5 Terms related to trustworthiness3.5.1 accountable3.5.2 accountability3.5.3 availability3.5.4 bias3.5.5 control3.5.6 controllability3.5.7 explainability3.5.8 predictability3.5.9 reliability3.5.10 resilience3.5.11 risk3.5.12 robustness3.5.13 stakeholder3.5.14 transparency3.5.15 transparency3.5.16 trustworthiness3.5.17 verification3.5.18 validation3.6 Terms related to natural language processing3.6.1 automatic summarization3.6.2 dialogue management3.6.3 emotion recognition3.6.4 information retrieval3.6.5 machine translation3.6.6 named entity recognition3.6.7 natural language3.6.8 natural language generation3.6.9 natural language processing3.6.10 natural language processing3.6.11 natural language understanding3.6.12 optical character recognition3.6.13 part-of-speech tagging3.6.14 question answering3.6.15 relationship extraction3.6.16 sentiment analysis3.6.17 speech recognition3.6.18 speech synthesis3.7 Terms related to computer vision3.7.1 computer vision3.7.2 face recognition3.7.3 image3.7.4 image recognitionClause 5, concepts 5.1 General5.2 From strong and weak AI to general and narrow AI5.3 Agent5.4 Knowledge5.5 Cognition and cognitive computing5.6 Semantic computing5.7 Soft computing5.8 Genetic algorithms5.9 Symbolic and subsymbolic approaches for AI5.10 Data5.11 Machine learning concepts Home / Vocabulary Terms related to neural networks · 3.4.4 # deep learning an approach that builds rich hierarchical representations by training neural networks that have many hidden layers - definition - Defined term - ISO/IEC 22989:2022/FDAmd 1 - Last updated: 2026-10-21 ## What this term settles The definition is deliberately narrow. It tells the reader what counts as deep learning and leaves the engineering choices outside the word. ## Who it binds Nothing here creates an obligation by itself, and the obligation for deep learning arrives when the firm writes the term into a control, a specification or a purchase order. ## What to do in your practice Take the firm's own definition of deep learning, put it beside this one and mark the differences. Each difference is either a deliberate local choice or an inconsistency to fix. ## Evidence an auditor or regulator asks for - A controlled vocabulary entry for deep learning with the citation 3.4.4, an owner and a review date. An auditor can check that deep learning is controlled rather than assumed. - A model architecture note that names the network form and the layers used. The row names deep learning. Shows deep learning in use in the artefact the firm relies on, not only in a glossary. - A test record that shows the behaviour the architecture was chosen for. The record carries the deep learning entry and its date. The architecture note uses the network name as a marketing label with no layer or training detail behind it. The gap is tested against deep learning. Ask your teamWhere does the firm use the term deep learning, and can the owner point to the entry it relies on? ## Common gaps this page catches - deep learning is used in the network architecture note without a written definition, so two teams can mean different things. - The entry for deep learning is reviewed when someone remembers, not on a cadence, so it drifts from the artefact it describes. ## What to prepare before the amendment lands Put deep learning on the agenda of the next AI governance meeting with two questions: where is it used, and what would show it is used correctly? Record the answers as actions. ## How to test it here Take one live artefact rather than a template, find the sentence or field where deep learning is used, and put this page beside it. Record the answer, the artefact and the date, because that record is the evidence and a glossary entry on its own is only an intention. ## What this page does not claim The page does not reproduce the standard, certify the firm or predict the wording the amendment will use; it states deep learning at the claim level "definition" and stops where the public record stops. ## Common questions What does deep learning mean in ISO 22989? an approach that builds rich hierarchical representations by training neural networks that have many hidden layers What evidence should we keep for 3.4.4? A controlled vocabulary entry for deep learning with the citation 3.4.4, an owner and a review date. A model architecture note that names the network form and the layers used. The row names deep learning. Is deep learning a requirement? No: deep learning is a definition or clause of a terminology standard, and it becomes binding only when a firm writes it into a policy, a contract or a procedure, or when another standard incorporates it. ## Related units - 3.4.3 convolution - 3.4.5 exploding gradient - 3.4.6 feed forward neural network Source. ISO/IEC 22989:2022, Clause 3.4.4, Terms and definitions. Open the ISO public entry. Retrieved 21 October 2026. https://www.iso.org/obp/ui/en/#iso:std:iso-iec:22989:ed-1:v1:en ISO catalogue record 88145, the amendment status, stage and dates. Catalogue record 74296, the base standard. Claim level: definition. The definition is in the public preview. This page states it in our own words and does not reproduce the text. Built from the public ISO record and the public clause 3 entries. No clause text is reproduced. How the record is built. Last updated: 2026-10-21 · Markdown version · Check your vocabulary Source: https://iso-22989-revision-checker.theartofservice.com/glossary/deep-learning/