TF Conceptual Parity Note (Keras Map of L1-L5, No TF Lab)

Map 5 PyTorch ops to Keras equivalents. Mapping table.

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PyTorch-only labs; this note maps the five PyTorch concepts to Keras equivalents so TF-shop interviews hold no surprises. Reading-level only.

Mapping table

PyTorch (labs)	Keras equivalent	Note
Tensor + autograd	`tf.Tensor` + `GradientTape`	Tape scopes the watched ops
`nn.Module` + training loop	`Model.fit` / custom `train_step`	fit hides the loop; know both
Conv2d/MaxPool stacks	`Conv2D`/`MaxPooling2D`	Same receptive-field math
Embedding + LSTM/GRU + masking	`Embedding(mask_zero=True)` + `LSTM/GRU`	Masking semantics match
Freeze (`requires_grad=False`) + LR groups	`layer.trainable=False` + optimizers	Same discipline, new API

Five rows memorized with the API asymmetry per row (Tape scoping, fit-vs-loop, mask_zero) — enough to whiteboard either framework.

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