Category:Machine learning
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English: Machine learning is a branch of statistics and computer science, which studies algorithms and architectures that learn from observed facts.
scientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions | |||
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Instance of | academic discipline | ||
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Subclass of | computer science, artificial intelligence | ||
Part of | artificial intelligence | ||
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Subcategories
This category has the following 31 subcategories, out of 31 total.
A
- Automated pattern recognition (22 F)
C
- Case-based reasoning (5 F)
- Computational creativity (8 F)
- Cross-validation (statistics) (16 F)
D
- Data spirals (6 F)
E
G
I
- Inductive logic programming (4 F)
K
M
- Markov models (19 F)
N
- Neural-Style (16 F)
O
- Overfitting (10 F)
P
R
- Reinforcement learning (19 F)
S
- Support vector machine (21 F)
T
U
- Underfitting (2 F)
V
- Vowpal Wabbit (2 F)
Pages in category "Machine learning"
This category contains only the following page.
Media in category "Machine learning"
The following 200 files are in this category, out of 353 total.
(previous page) (next page)- 1D Convolution.png 321 × 310; 11 KB
- 1D Convolutional Neural Network feed forward example.png 661 × 301; 31 KB
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- A hybrid deep learning approach for medical relation extraction.pdf 1,275 × 1,650, 4 pages; 570 KB
- A simple Decision Tree.png 828 × 828; 76 KB
- Accure Momentum Cluster.png 692 × 342; 45 KB
- ActivationFunctions.svg 1,058 × 606; 293 KB
- Adjacent sampling method for training and testing dataset.png 1,672 × 1,418; 3.2 MB
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- AI End-Scenario Six Avenues for AI takeover tamingtheaibeast.png 3,285 × 2,131; 547 KB
- Alpha Coach Logo.png 2,779 × 867; 47 KB
- Analogical modeling pointer network.svg 310 × 300; 29 KB
- Andrews curve for Iris data set.png 1,150 × 742; 334 KB
- Apache SystemML logo.svg 512 × 160; 921 bytes
- Aprendizado por Reforço.png 1,000 × 1,000; 94 KB
- Aprendizagem não supervisionada.png 1,613 × 2,100; 210 KB
- Aprenentatge automàtic.jpg 920 × 682; 40 KB
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- Arbana Consulting Group.png 372 × 180; 17 KB
- Artificial grammar learning example.jpg 462 × 252; 28 KB
- Artificial Neural Network with Chip.jpg 2,000 × 1,600; 2.59 MB
- Artificial Neural Network with Chip.png 1,257 × 943; 1.9 MB
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- ATW CNN architecture.png 3,360 × 5,019; 807 KB
- Augmentation Content Curation DRAFT.pdf 1,275 × 1,650, 7 pages; 261 KB
- Augmentation Content Generation DRAFT.pdf 1,275 × 1,650, 6 pages; 260 KB
- Augmentation Governance DRAFT.pdf 1,275 × 1,650, 5 pages; 284 KB
- Augmentation Machine Translation DRAFT.pdf 1,275 × 1,650, 8 pages; 1.25 MB
- Augmentor.png 330 × 182; 25 KB
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- Base de dados de flores de íris.svg 512 × 486; 208 KB
- Bayessches Netz.png 607 × 250; 4 KB
- BPM input space wiki.pdf 1,239 × 1,752; 381 KB
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- C.EstelleSmith ResearchShowcase 5 20 20.pdf 1,500 × 1,125, 50 pages; 2.23 MB
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- Climate data analysis using tSNE method.png 814 × 805; 94 KB
- CollieBrownArghonCEO.jpg 965 × 1,241; 307 KB
- Compactness Comparison of Linear and Multilinear Projections.png 791 × 614; 65 KB
- Concept lattice.svg 693 × 513; 70 KB
- Confusion matrix.png 1,557 × 805; 82 KB
- Contexts venn diagramm for analogical modeling.svg 600 × 582; 10 KB
- Contraintes dans l'espace.png 298 × 236; 15 KB
- Conv layers uk.png 567 × 310; 42 KB
- Convolutional-neural-network-polyanalyst-flowchart-example.png 561 × 190; 20 KB
- Convolutional-neural-network-polyanalyst-flowchart-example.svg 891 × 357; 280 KB
- ConvolutionAndPooling.svg 839 × 208; 160 KB
- CorteX Carte Predpol.jpg 446 × 196; 93 KB
- Data Augmentation of rock images revised.jpg 1,280 × 1,440; 472 KB
- Data Augmentation of rock images.jpg 1,522 × 1,128; 288 KB
- DatasetSample.png 1,586 × 1,066; 2.2 MB
- David Weinberger with blue checks - 2019.png 2,793 × 2,845; 11.34 MB
- Dean Weber.png 957 × 931; 1.76 MB
- Decaying Sine Unit (DSU).png 3,000 × 2,000; 420 KB
- DeepInsight method to transform non-image data to 2D image for convolutional neural network architecture.pdf 1,239 × 1,629, 7 pages; 1.78 MB
- DeepLearningReconstruction.png 2,502 × 600; 617 KB
- DenseCap (Johnson et al., 2016) (cropped).png 702 × 495; 489 KB
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- Diagram of the mountain car problem.png 435 × 169; 3 KB
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- Dragonfly-Trainable-segmentation-workspace.png 1,904 × 955; 316 KB
- Duplication attributs.png 562 × 188; 5 KB
- EBM small.gif 1,080 × 1,080; 12.79 MB
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- EfficientNetB3V2.jpg 720 × 720; 66 KB
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- Employee Attrition Prediction.pdf 1,275 × 1,650, 3 pages; 619 KB
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- Esquema 1.svg 512 × 298; 64 KB
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- Esquema aprendizaje.jpg 920 × 682; 34 KB
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- Example for Adjusted Rand index.svg 900 × 360; 467 KB
- Feature selection Embedded Method.png 1,279 × 379; 46 KB
- Feature selection Wrapper Method.png 1,220 × 342; 41 KB
- FeatureSelectionToolbox1 screenshot.png 1,025 × 740; 46 KB
- Federated learning process central case.png 906 × 435; 73 KB
- Filter Methode uk.png 940 × 107; 8 KB
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- Flores de Íris.png 1,945 × 977; 2.04 MB
- Flowchart of the spatio-temporal action localization detector Segment-tube.png 3,934 × 3,280; 1.93 MB
- FreqGenSchema.png 1,408 × 829; 28 KB
- FullSLAM.png 600 × 396; 15 KB
- Further-Machine-Learning-Resources-1-680x340.png 680 × 340; 187 KB
- Gaussian process draws from prior distribution.png 1,200 × 400; 112 KB
- Gaussian Process Regression.png 1,200 × 400; 98 KB
- Gaussian training data.png 512 × 512; 28 KB
- GaussianScatterPCA.svg 720 × 720; 515 KB
- Gcp docai platform.1000064920000870.max-2000x2000 (2).png 2,000 × 870; 204 KB
- Glue Ontologie Beispiel.png 400 × 106; 19 KB
- Glue-Architektur.png 297 × 509; 28 KB
- GMilovanovic eRum2018.pdf 1,654 × 1,239, 14 pages; 1.02 MB
- GNN building blocks.png 7,810 × 2,010; 740 KB
- GNN representational limits.png 3,430 × 3,510; 502 KB
- Google cloud partner .jpg 860 × 680; 21 KB
- Growing Cosine Unit (GCU) activation function.png 3,000 × 2,186; 273 KB
- Halodi Robotics' Perception Engineer With a Humanoid Collaborative Robot.jpg 3,024 × 3,024; 1.12 MB
- Hazardconf.png 261 × 163; 167 KB
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- Hinge loss variants.svg 720 × 540; 21 KB
- Hinge loss vs zero one loss.svg 720 × 540; 15 KB
- Hyperparameter Optimization using Grid Search.svg 540 × 360; 72 KB
- Hyperparameter Optimization using Random Search.svg 540 × 360; 74 KB
- Idistance.jpg 601 × 351; 26 KB
- Image Content filtration - Outreachy.pdf 2,000 × 1,125, 32 pages; 931 KB
- Info Gain Root Split Example.png 281 × 351; 15 KB
- Info Gain Splitting the Child Node(s) Example.png 361 × 511; 23 KB
- Integrated-stacking.png 8,192 × 3,807; 1.11 MB
- Intersection over Union - object detection bounding boxes.jpg 600 × 450; 92 KB
- Intersection over Union - poor, good and excellent score.png 600 × 248; 8 KB
- Intersection over Union - visual equation.png 600 × 468; 11 KB
- Iris Flowers Clustering kMeans de.svg 660 × 309; 145 KB
- Iris Flowers Clustering kMeans ru.svg 660 × 309; 50 KB
- Iris Flowers Clustering kMeans.svg 660 × 309; 145 KB
- Isat.gif 360 × 252; 1.08 MB
- K-fold cross validation EN.svg 521 × 257; 246 KB
- K-fold cross validation UK.svg 521 × 257; 247 KB
- K-Fold Cross-Validation.png 835 × 384; 44 KB
- Katonic.ai.png 1,719 × 365; 45 KB
- Keras model inner2 epochs10000.svg 575 × 431; 731 KB
- Keras model inner32 epochs500.svg 575 × 431; 934 KB
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- Kmedoid2.jpg 560 × 420; 17 KB
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- Knowledge Graph Embedding.pdf 1,293 × 585; 182 KB
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- Kompromis między obciążeniem a wariancją – dekompozycja.svg 375 × 375; 82 KB
- L1-PCA.png 1,750 × 1,313; 82 KB
- Labelled points in feature space.jpg 425 × 425; 30 KB
- Latent Dirichlet allocation.svg 593 × 311; 15 KB
- Lattice of automata accepting 1, 10, and 100.gif 1,166 × 836; 31 KB
- Learn gate concept.png 250 × 250; 16 KB
- Learners.jpg 327 × 88; 16 KB
- Learning Curves (Naive Bayes).png 640 × 480; 41 KB
- Least Squares Policy Iteration.svg 403 × 227; 42 KB
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- LinearneSeparovatelne.png 435 × 386; 14 KB
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- Loss function surrogates.svg 512 × 512; 40 KB
- Lstm block.svg 395 × 455; 32 KB
- LSTM Cell.svg 512 × 350; 20 KB
- Machine Learning cookies.jpg 4,032 × 3,024; 3.63 MB
- Machine Learning Icon.jpg 358 × 205; 17 KB
- Machine learning Roadmap.png 1,268 × 1,534; 153 KB
- Machine learning workflow diagram.png 603 × 373; 26 KB
- Machinelearning.jpg 2,000 × 1,600; 2.59 MB
- MachineLearningLinearAssociativeNetwork.webm 2 min 13 s, 692 × 592; 3.38 MB
- MachineLearningPanelDiscussionAtIEEETechIgnite2017.jpg 4,128 × 2,322; 2.73 MB
- MarketingMLTaxonomy.png 3,010 × 2,250; 307 KB
- MarkovBlanket.png 264 × 299; 24 KB
- Matrice de donnees.png 292 × 190; 3 KB
- Matriz confusion binaria.jpg 255 × 166; 13 KB
- Max pooling uk.png 570 × 330; 20 KB
- Max x 5 vs softmax.jpg 806 × 588; 39 KB
- Message Passing Neural Network.pdf 422 × 437; 39 KB
- Message Passing Neural Network.png 2,817 × 2,910; 426 KB
- Mesterségesnyelvtan-elsajátítás.jpg 591 × 345; 20 KB
- Metodo de retención.jpg 529 × 159; 72 KB
- Michie-MENACE-graph.png 1,440 × 1,275; 107 KB
- ML dataset training validation test sets.png 1,280 × 558; 31 KB
- ML Ops Venn Diagram.svg 512 × 368; 27 KB
- Mldemo sous WSL.png 638 × 507; 49 KB
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- Multilayer Perceptron with one hidden layer.svg 293 × 248; 40 KB
- Multilinear projection for dimension reduction of tensor.png 994 × 509; 26 KB
- Multimodal Compact Multilinear Pooling.png 639 × 333; 48 KB
- Naive corral.png 647 × 518; 9 KB
- NeedForDeeperLayers.svg 834 × 547; 692 KB
- New Instance.jpg 340 × 28; 11 KB