feat: complete wikification of War Commander batch 1&2 and final grey dot cleanup

10_Wiki/Topics/AI/L2-Regularization.md

@@ -1,6 +1,6 @@
 ---
 id: P-REINFORCE-AUTO-L2RE-001
-category: "[[10_Wiki/💡 Topics/AI]]"
+category: "10_Wiki/💡 Topics/AI"
 confidence_score: 0.97
 tags: [auto-reinforced, l2-regularization, machine-learning, deep-learning, overfitting, weight-decay]
 last_reinforced: 2026-04-20
@@ -25,6 +25,6 @@ L2 정규화(L2-Regularization) 혹은 Ridge 정규화는 모델의 복잡도를
 - **정책 변화(RL Update)**: 거대 모델 정책(Foundation-Models)에서는 파라미터가 너무 많아 정규화가 필수적이지만, 단순히 가중치를 줄이는 것을 넘어 '드롭아웃(Dropout)'이나 '데이터 증강' 등 다양한 정책과 혼합하여 사용됨.
 
 ## 🔗 지식 연결 (Graph)
-- [[Gradient-Descent]], [[Optimization]], [[Deep Learning (DL)]], [[Efficiency]], [[Scaling-Laws]]
+- [[Gradient-Descent]], [[Optimization]], Deep Learning (DL), [[Efficiency]], Scaling-Laws
 - **Modern Tech/Tools**: Ridge regression, Weight decay in PyTorch/TensorFlow, AdamW optimizer.
 ---