Definition
In neural networks, ‘dense’ refers to fully connected layers where each neuron receives input from all neurons in the preceding layer. This contrasts with sparse connections found in convolutional or recurrent architectures. Dense layers are fundamental for learning complex non-linear mappings between inputs and outputs, serving as the primary mechanism for feature integration and decision-making in feedforward networks.
Summary
A layer or tensor where every element is connected to every element of the previous layer or dimension.
Key Concepts
- Fully Connected
- Weight Matrix
- Activation Function
- Feature Integration
Use Cases
- Final classification layers in MLPs
- Feature fusion in hybrid models
- Simple regression tasks
Code Example
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