Deep Learning · TEE · section
Images and sequences
Course vocabulary in plain words
| Term | Meaning | What to keep separate |
|---|---|---|
| Tensor | Array of numbers with one or more axes | Its shape and its values |
| Channel | One feature map within an image-like tensor | Channels versus spatial size |
| Kernel / filter | Learned weights sliding over a map | Kernel size versus filter count |
| Stride | Step between adjacent window positions | Stride versus kernel width |
| Padding | Extra border cells around an input | Per-side P versus total 2P |
| Feature map | Responses from a filter across positions | Activations versus parameters |
| Pooling | Window-based reduction of feature maps | Maximum versus average |
| Flatten | Rearrange H × W × C entries as a vector | Rearrangement has no parameters |
| Logit | Raw score before an output activation | Score versus probability |
| Hidden state | Recurrent features carried through time | Previous state versus current input |
| Shared weights | One learned rule reused at many positions/times | Reuse versus extra parameters |