fix(deep-learning-book): address review nits — internal naming and SAME-padding disclosure

Two small findings from the automated review on PR #994, both verified against the
source before fixing.

1. reading_path_planner.py: the plan() parameter was named include_optional while
   the CLI flag and call site both use include_intro, and it only ever gates ch01.
   Renamed the parameter and its use for consistency. Behavior unchanged, confirmed
   both ways: --include-intro keeps ch01 first, the default drops it.

2. model_arithmetic.py: conv2d "same" padding computes ceil(H / stride), which is
   the TensorFlow/Keras SAME convention, and the tool did not disclose which
   framework it matches. Documented it in the module docstring — including that
   PyTorch's padding='same' is symmetric-only and rejects a stride other than 1, so
   a strided PyTorch layer will not match, with "valid" named as the exact-case
   workaround — plus a pointer comment at the computation itself. No arithmetic
   change; the sample stack reports identical parameters and FLOPs.

The reviewer's third point was that it could not execute the gates in a sandboxed
environment. No code change: those gates were run locally and are green.

Verified after the change: compileall, check_paths, check_frontmatter,
check_dual_publish, check_model_freshness, smoke_scripts (696 passed),
derive_counters --check, check_skill_names, check_plugin_json, the book-skill
validator, and --sample --output json on all four tools.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01BswsZp5zrJWFAGU6KWNA1s

engineering/deep-learning-book/skills/deep-learning-book/scripts/model_arithmetic.py

@@ -14,6 +14,12 @@ forward pass; a training step costs roughly 3x a forward pass (forward + backwar
 Layer types: input, linear, conv2d, pool2d, flatten, embedding, layernorm, activation,
 dropout, mha (multi-head self-attention), lstm, gru.
 
+conv2d "same" padding follows TensorFlow/Keras SAME: output is ceil(H / stride), with
+any needed padding split across the two sides (and the extra pixel going to the bottom
+and right at even kernel sizes). PyTorch's padding='same' is symmetric-only and rejects
+a stride other than 1, so a strided PyTorch layer will not match this row — declare it
+as "valid" with the padding you actually apply if you need that case exactly.
+
 Standard library only. No frameworks, no network calls.
 
 Exit codes:
@@ -87,6 +93,8 @@ def step(layer: dict, shape: tuple[int, ...], index: int) -> tuple[tuple[int, ..
             )
         channels, height, width = shape
         if padding == "same":
+            # TensorFlow/Keras SAME convention; see the module docstring for how this
+            # differs from PyTorch's stride-1-only padding='same'.
             out_h, out_w = math.ceil(height / stride), math.ceil(width / stride)
         elif padding == "valid":
             out_h = (height - kernel) // stride + 1

engineering/deep-learning-book/skills/deep-learning-book/scripts/reading_path_planner.py

@@ -242,12 +242,12 @@ def out_of_scope_hits(goal: str) -> list[str]:
 
 
 def plan(goal: str, background: str, hours_per_week: float,
-         include_optional: bool) -> dict:
+         include_intro: bool) -> dict:
     lane_key, _ = score_lanes(goal)[0]
     lane = LANES[lane_key]
     targets = lane["targets"]
     chapters = close_prerequisites(targets)
-    if not include_optional and lane_key != "complete":
+    if not include_intro and lane_key != "complete":
         # ch01 is context; keep it only when the reader asked for everything.
         chapters = [c for c in chapters if c != 1]
     ordered = order_path(chapters)