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Machine learning

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without being explicitly programmed. Advances in the field of deep learning have allowed neural networks, a class of statistical algorithms…

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Machine learning

Nodes495
Edges494
Triples272
Avg. degree2
Density0.00404
Components1

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Machine learning

Top relations

related to Conferences · 26
Machine learning → AAAI Conference, ACL, Artificial IntelligenceAssociation, Bioinformatics, Biostatistics, CIBB, Computational Intelligence Methods, Computational Linguistics, Conference, Data Mining, Databases, ECML PKDD, European Conference, ICLR, ICML, Intelligent Robots, International Conference, IROS, KDD, Knowledge Discovery
related to Model assessments · 16
Machine learning → Area Under, AUC, Classification, FNR, FPR, Higher AUC, However, In, K-1, K-fold-cross-validation, Receiver, ROC, ROC Curve, Similarly, TNR, TPR
related to Artificial intelligence · 15
Machine learning → AI, As, By, David Rumelhart, Geoffrey Hinton, However, ILP, In, John Hopfield, Neural, Probabilistic, Their, They, This, Work
see also · 14
Machine learning → Automated, Colab, Extremely, Field, Framework, IDE, JuliaList, List, Machine, Mathematical, ML, Process, Programming, Python
related to history · 13
Machine learning → Arthur Samuel, Behavior, Canadian, Donald Hebb, IBM, In, Other, Samuel, The, The Hebbian, The Organization, Walter Pitts, Warren McCulloch
related to Bias · 12
Machine learning → Another, Different, European-sounding, For, Geolitica's, George's Medical School, Racial Equality, St, Systems, UK's Commission, Using, When
related to Tensor Processing Units (TPUs) · 11
Machine learning → AI, FPGAs, Google, Google Cloud AI, Google's DeepMind AlphaFold, GPUs, Since, Tensor Processing Units, They, TPUs, Unlike
related to Explainability · 9
Machine learning → AI, AI-powered, By, Explainable AI, Explainable Machine Learning, Interpretable AI, It, XAI, XML
related to Hardware · 9
Machine learning → AI, AI-specific, AlexNet, AlphaZero, By, CPUs, GPUs, OpenAI, Since
related to Data compression · 8
Machine learning → An, Conversely, For, LZ77, LZW, PPM, There, This

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Important terminology

learning machine data algorithms model training models artificial used set classification systems methods also neural ai feature example algorithm field

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
Machine learningis ageneral term for any machine learning method that identifies0.90text
Machine learningis asub-field of machine learning where models are deployed on embedded systems with limited computing resources0.90text
image compression.Data compression aims to reduce the size of data filesinstance ofThis technique simplifies handling extensive datasets that lack predefined labels and finds widespread use in fields0.80text
enhancing storage efficiencyinstance ofThis technique simplifies handling extensive datasets that lack predefined labels and finds widespread use in fields0.80text
speeding up data transmissioninstance ofThis technique simplifies handling extensive datasets that lack predefined labels and finds widespread use in fields0.80text
Portable Network Graphicsinstance ofoutperforming conventional methods0.80text
predicting a person's height based on factors like ageinstance ofregression is used for tasks0.80text
genetics or forecasting future temperatures based on historical data.Similarity learning is an area of supervised machine learning closely related to regressioninstance ofregression is used for tasks0.80text
classificationinstance ofregression is used for tasks0.80text
but the goal is to learn from examples using a similarity function that measures how similar or related two objects areinstance ofregression is used for tasks0.80text
classification often require input that is mathematicallyinstance ofIt has been argued that an intelligent machine learns a representation that disentangles the underlying factors of variation that explain the observed data.Feature learning is m…0.80text
computationally convenient to processinstance ofIt has been argued that an intelligent machine learns a representation that disentangles the underlying factors of variation that explain the observed data.Feature learning is m…0.80text

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