Convolutional layers extract meaningful patterns from sequential or time-series data. They apply learnable filters across the input to detect local structures and important signal characteristics.
| Layer | Description | Parameters |
|---|---|---|
| Conv1D | Creates feature maps using convolutional filters to highlight important patterns in sequential or time-series data. | Filters — Number of convolutional filters (feature detectors) in this layer. Kernel Size — Size of the sliding window (filter) used to extract features. Activation — Function applied to the output. Available options: Linear, ReLU, Sigmoid, Softmax, Tanh. |