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Convolutional neural network - Wikipedia
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Convolutional neural network - Wikipedia
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| title | Convolutional neural network - Wikipedia |
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Overskrifter
| 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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| "Deconvolution and Checkerboard Artifacts" | Ekstern | noFollow |
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| "Comparing Object Recognition in Humans and Deep Convolutional Neural Networks—An Eye Tracking Study" | Ekstern | noFollow |
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| ^ | Intern | Sender Juice |
| Schmidhuber, Juergen | Intern | Sender Juice |
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| cs.NE | Ekstern | noFollow |
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| "Deep learning" | Ekstern | noFollow |
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| Phoneme Recognition Using Time-Delay Neural Networks | Ekstern | noFollow |
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| Wayback Machine | Intern | Sender Juice |
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| "Convolutional networks for images, speech, and time series" | Ekstern | noFollow |
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| Connectionist Architectures for Multi-Speaker Phoneme Recognition | Ekstern | noFollow |
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| Neural network recognizer for hand-written zip code digits | Ekstern | noFollow |
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| "Deep Learning" | Ekstern | noFollow |
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| ^ | Intern | Sender Juice |
| "Error Back Propagation with Minimum-Entropy Weights: A Technique for Better Generalization of 2-D Shift-Invariant NNs" | Ekstern | noFollow |
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| Applications of neural networks to medical signal processing | Ekstern | noFollow |
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| "Using GPUs for Machine Learning Algorithms" | Ekstern | noFollow |
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| "CS231n Convolutional Neural Networks for Visual Recognition" | Ekstern | noFollow |
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| "Dropout: A Simple Way to Prevent Neural Networks from Overfitting" | Ekstern | noFollow |
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| "The inside story of how AI got good enough to dominate Silicon Valley" | Ekstern | noFollow |
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| "A Convolutional Neural Network Approach for Objective Video Quality Assessment" | Ekstern | noFollow |
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| "Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning" | Ekstern | noFollow |
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