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Convolutional neural network - Wikipedia
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| Property | Content |
|---|---|
| title | Convolutional neural network - Wikipedia |
| type | website |
Headings
| H1 | H2 | H3 | H4 | H5 | H6 |
| 1 | 19 | 40 | 14 | 0 | 0 |
- [H1] Convolutional neural network
- [H2] Contents
- [H2] Architecture
- [H2] History
- [H2] Distinguishing features
- [H2] Building blocks
- [H2] Hyperparameters
- [H2] Translation equivariance and aliasing
- [H2] Evaluation
- [H2] Regularization methods
- [H2] Hierarchical coordinate frames
- [H2] Applications
- [H2] Fine-tuning
- [H2] Human interpretable explanations
- [H2] Related architectures
- [H2] Notable libraries
- [H2] See also
- [H2] Notes
- [H2] References
- [H2] External links
- [H3] Convolutional layers
- [H3] Pooling layers
- [H3] Fully connected layers
- [H3] Receptive field
- [H3] Weights
- [H3] Deconvolutional
- [H3] Receptive fields in the visual cortex
- [H3] Fukushima's analog threshold elements in a vision model
- [H3] Neocognitron, origin of the trainable CNN architecture
- [H3] Convolution in time
- [H3] Time delay neural networks
- [H3] Image recognition with CNNs trained by gradient descent
- [H3] Shift-invariant neural network
- [H3] GPU implementations
- [H3] Convolutional layer
- [H3] Pooling layer
- [H3] ReLU layer
- [H3] Fully connected layer
- [H3] Loss layer
- [H3] Padding
- [H3] Stride
- [H3] Number of filters
- [H3] Filter (or kernel) size
- [H3] Pooling type and size
- [H3] Dilation
- [H3] Empirical
- [H3] Explicit
- [H3] Image recognition
- [H3] Video analysis
- [H3] Natural language processing
- [H3] Animal behavior detection
- [H3] Anomaly detection
- [H3] Drug discovery
- [H3] Checkers game
- [H3] Go
- [H3] Time series forecasting
- [H3] Cultural heritage and 3D-datasets
- [H3] Deep Q-networks
- [H3] Deep belief networks
- [H3] Neural abstraction pyramid
- [H4] Max pooling
- [H4] LeNet-5
- [H4] Local connectivity
- [H4] Spatial arrangement
- [H4] Parameter sharing
- [H4] Channel max pooling
- [H4] Dropout
- [H4] DropConnect
- [H4] Stochastic pooling
- [H4] Artificial data
- [H4] Early stopping
- [H4] Number of parameters
- [H4] Weight decay
- [H4] Max norm constraints
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| "Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network" | External | noFollow |
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| "Error Back Propagation with Minimum-Entropy Weights: A Technique for Better Generalization of 2-D Shift-Invariant NNs" | External | noFollow |
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| "The Potential of the Intel (R) Xeon Phi for Supervised Deep Learning" | External | noFollow |
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| "A Comparison of Pooling Methods for Convolutional Neural Networks" | External | noFollow |
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| "A theory of steady-state activity in nerve-fiber networks: I. Definitions and preliminary lemmas" | External | noFollow |
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| "Deep Learning With Conformal Prediction for Hierarchical Analysis of Large-Scale Whole-Slide Tissue Images" | External | noFollow |
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| "A Convolutional Neural Network Approach for Objective Video Quality Assessment" | External | noFollow |
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| "The Face Detection Algorithm Set To Revolutionize Image Search" | External | noFollow |
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| "Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation" | External | noFollow |
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| "DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels" | External | noFollow |
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| "Automated monitoring of honey bees with barcodes and artificial intelligence reveals two distinct social networks from a single affiliative behavior" | External | noFollow |
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| "Action Recognition by an Attention-Aware Temporal Weighted Convolutional Neural Network" | External | noFollow |
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| "Convolutional Deep Belief Networks on CIFAR-10" | External | noFollow |
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| "Introduction to Machine Learning, Neural Networks, and Deep Learning" | External | noFollow |
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| CS231n: Convolutional Neural Networks for Visual Recognition | External | noFollow |
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| vdumoulin/conv_arithmetic: A technical report on convolution arithmetic in the context of deep learning | External | noFollow |
| Artificial intelligence | Internal | Passing Juice |
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