3,135 articles
Deadlock detection
Deadlock detection is the runtime activity of determining that transactions have entered a deadlock, usually by finding a cycle in a wait-for graph of waiting transactions.
DeBERTa
DeBERTa is a pre-trained language model developed by Microsoft Research that improves on BERT and RoBERTa, and in 2021 became the first single model to surpass human performance on SuperGLUE.
Debezium
Debezium is an open-source log-based change data capture (CDC) platform begun at Red Hat in 2015, now under the Commonhaus Foundation. It streams row-level database changes as ordered events via Kafka Connect, Debezium Server, or an embedded Java engine.
Decentralized federated learning
Decentralized federated learning, also called swarm learning, is a machine learning approach where clients train a shared model by exchanging updates directly with peers, without a central server.
Decentralized partially observable Markov decision process
A decentralized partially observable Markov decision process (Dec-POMDP) is a mathematical model for a team of cooperating agents under uncertainty, where each agent acts on its own local observations toward a shared reward.
Deciphering Data Architectures
Deciphering Data Architectures (O'Reilly, February 2024, 278 pages) by James Serra, Microsoft big data and data warehousing solution architect, compares modern data warehouse, data fabric, data lakehouse, and data mesh architectures, with pros, cons, and use cases.
Decision table
A decision table is a rule-based classifier that stores labeled instances in a table of conditions and outcomes, classifying new instances by looking up the matching cell's majority class.
Decision tree
A decision tree is a machine learning model that predicts outcomes by recursively splitting data with simple feature questions, used for classification and regression tasks.
Decision tree learning
Decision tree learning is a supervised machine learning method that builds a predictive model as a tree of feature tests, handling classification and regression, and used in ensembles like random forests.
Decision tree model
The decision tree model is a supervised learning model that predicts by learning a hierarchy of decision rules, each node testing a feature and each leaf giving a prediction.
Decision Trees
Decision trees: branching diagrams of decision nodes (squares), chance nodes (circles), probabilities summing to 1.0, and valued outcomes, used in clinical decision analysis, health economics, and machine learning classification.
Decision-focused learning
Decision-focused learning (DFL) is a machine learning approach that trains a predictive model end-to-end so its predictions lead to good decisions in a downstream optimization problem.
Declarative data pipelines
Declarative data pipelines define datasets and transformations as declared queries, letting engines like Spark Declarative Pipelines, Snowflake Dynamic Tables, dbt, and Lakeflow infer DAGs, scheduling, and incremental processing automatically.
Deconvolution
Deconvolution is the operation inverse to convolution, recovering an input signal from an observed output. Because it amplifies noise, regularized methods like Wiener and Richardson-Lucy filtering are used.
Deep active learning
Deep active learning trains deep neural networks with fewer labeled examples by letting the model choose which unlabeled samples a human annotator should label next, in repeated pool-based rounds.
Deep belief network
A deep belief network (DBN), or deep belief net, is a generative neural network with many layers of hidden variables, introduced by Hinton, Osindero, and Teh in 2006.
Deep clustering
Deep clustering is a family of machine learning methods that jointly learns a neural network representation and a clustering of unlabeled data, exemplified by Deep Embedded Clustering (DEC).
Deep convolutional GAN
A deep convolutional GAN (DCGAN) is a generative adversarial network built from convolutional layers, introduced by Radford, Metz, and Chintala in 2015 and long the baseline GAN.
Deep ensembles
A deep ensemble is a set of independently trained neural networks whose averaged predictions provide uncertainty estimates, introduced by Lakshminarayanan and colleagues at DeepMind in 2016.
Deep generative model
A deep generative model is a neural network method that learns the probability distribution of training data and draws new samples resembling it, including GANs, VAEs, and diffusion models.
Deep hashing
Deep hashing is a machine learning method that trains a deep neural network to map inputs, typically images, to compact binary codes for fast similarity search.
Deep knowledge tracing
Deep knowledge tracing (DKT) is a machine learning method, reported by Chris Piech and colleagues in 2015, that uses a recurrent neural network to model a student's evolving knowledge state.
Deep learning
Deep learning is a subset of machine learning using multi-layer neural networks that learn patterns from data without hand-programmed features, powering speech and image recognition.
Deep learning speech synthesis
Deep learning speech synthesis generates spoken audio from text using neural networks, replacing rule-based and concatenative methods and producing speech nearly indistinguishable from human speech, including zero-shot voice cloning.
Deep learning super sampling
Deep learning super sampling (Nvidia DLSS) is an Nvidia AI technology that raises game frame rates by reconstructing high-resolution images from fewer rendered pixels on RTX GPUs.
Deep matrix factorization
Deep matrix factorization (DMF) is a machine learning method that represents a data matrix through a sequence of stacked linear factorizations, used in recommender systems, clustering, and image restoration.
Deep metric learning
Deep metric learning trains a deep neural network to map inputs into an embedding space where distances reflect semantic similarity, used for retrieval, verification, and clustering.
Deep operator network
A deep operator network, or DeepONet, is a neural network architecture in scientific machine learning that learns mappings between function spaces, approximating solutions of partial differential equations.
Deep Potential Smooth Edition
Deep Potential Smooth Edition (DeepPot-SE) is an end-to-end machine learning model of the inter-atomic potential energy surface, developed by Linfeng Zhang, Jiequn Han, and Weinan E, among others.
Deep Q-learning
Deep Q-learning is a reinforcement learning method using a deep neural network to approximate action values; its DQN agent reached human-level play on Atari games.