refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조
에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
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id: wiki-2026-0508-perceptrons-foundations
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title: Perceptrons-Foundations
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Perceptron, Rosenblatt Perceptron, MLP]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.95
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verification_status: applied
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tags: [perceptron, neural-network, mlp, history, foundations]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: python
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framework: pytorch, numpy
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---
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# Perceptrons-Foundations
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## 매 한 줄
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> **"매 weighted sum + threshold = NN의 atom"**. Rosenblatt 1957 perceptron — 매 first trainable neuron model. Single-layer 의 XOR fail (Minsky 1969) → AI winter. MLP + backprop (1986) 의 revival. 매 modern transformer 도 결국 stacked perceptron.
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## 매 핵심
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### 매 history
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- 1943: McCulloch-Pitts neuron (binary, no learning).
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- 1957: Rosenblatt perceptron — 매 hardware Mark I, learnable weights.
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- 1969: Minsky & Papert "Perceptrons" — 매 XOR limit proven → first AI winter.
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- 1986: Rumelhart, Hinton, Williams — 매 backprop revives MLP.
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- 2012: AlexNet — 매 deep MLP/CNN era 시작.
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### 매 perceptron 수학
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- `y = step(w·x + b)` where step(z) = 1 if z ≥ 0 else 0.
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- Update rule (Rosenblatt): `w ← w + η(y_true - y_pred)x`.
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- Convergence theorem: 매 linearly separable data 에 한해 finite steps 수렴.
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- Limit: 매 XOR (non-linearly separable) 학습 불가.
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### 매 multi-layer (MLP)
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- Hidden layer + nonlinearity (sigmoid → ReLU → GELU).
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- Universal approximation theorem (Cybenko 1989, Hornik 1991): 매 single hidden layer with enough units 가 매 continuous function 근사 가능.
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- Training: backprop (chain rule으로 gradient 계산).
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### 매 modern lens
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- Transformer FFN block = 2-layer MLP per token.
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- ViT, MLP-Mixer 등 매 pure-MLP 의 vision SOTA 도전.
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- 매 every "neural network" 의 atomic unit — perceptron.
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### 매 응용
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1. Pedagogical (NN intro).
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2. Linear classifier (single perceptron).
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3. Building block (MLP in transformer).
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4. Mixture-of-Experts: each expert = MLP.
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## 💻 패턴
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### Perceptron from scratch
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```python
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import numpy as np
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class Perceptron:
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def __init__(self, n_features, lr=0.1):
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self.w = np.zeros(n_features)
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self.b = 0.0
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self.lr = lr
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def predict(self, x):
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return 1 if x @ self.w + self.b >= 0 else 0
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def fit(self, X, y, epochs=100):
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for _ in range(epochs):
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for xi, yi in zip(X, y):
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pred = self.predict(xi)
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err = yi - pred
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self.w += self.lr * err * xi
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self.b += self.lr * err
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```
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### XOR fails for single perceptron
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```python
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X = np.array([[0,0],[0,1],[1,0],[1,1]])
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y = np.array([0, 1, 1, 0]) # XOR
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p = Perceptron(2)
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p.fit(X, y, epochs=1000)
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# Will NOT converge — XOR is not linearly separable
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```
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### MLP solves XOR
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```python
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import torch.nn as nn
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mlp = nn.Sequential(
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nn.Linear(2, 4), nn.ReLU(),
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nn.Linear(4, 1), nn.Sigmoid(),
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)
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# Train with BCELoss + Adam — converges in <1000 steps
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```
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### Transformer FFN = MLP per token
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```python
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class FFN(nn.Module):
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def __init__(self, dim, hidden):
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super().__init__()
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self.up = nn.Linear(dim, hidden)
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self.down = nn.Linear(hidden, dim)
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def forward(self, x):
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return self.down(nn.functional.gelu(self.up(x)))
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```
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### MLP-Mixer style (pure MLP vision)
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```python
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class MixerBlock(nn.Module):
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def __init__(self, n_patches, dim):
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super().__init__()
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self.token_mix = nn.Sequential(nn.Linear(n_patches, n_patches*4),
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nn.GELU(), nn.Linear(n_patches*4, n_patches))
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self.channel_mix = nn.Sequential(nn.Linear(dim, dim*4),
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nn.GELU(), nn.Linear(dim*4, dim))
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def forward(self, x): # (B, N, D)
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x = x + self.token_mix(x.transpose(1,2)).transpose(1,2)
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x = x + self.channel_mix(x)
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return x
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Linearly separable | Single perceptron OK |
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| Non-linear pattern | MLP (>=1 hidden layer) |
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| Tabular data | Tree models (XGBoost) usually beat MLP |
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| Image | CNN or ViT (still MLP-based) |
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| Sequence | Transformer (MLP + attention) |
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| Pedagogical | Start with perceptron history |
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**기본값**: 매 modern model 의 building block 으로 MLP 이해.
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## 🔗 Graph
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- 변형: [[MLP]]
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- 응용: [[MoE]]
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- Adjacent: [[데이터 사이언스 및 ML 엔지니어링|Backpropagation]]
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## 🤖 LLM 활용
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**언제**: 매 NN fundamentals, debugging gradient flow, designing custom architectures.
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**언제 X**: 매 production tabular tasks (use GBDT instead).
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## ❌ 안티패턴
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- **Linear activation only**: 매 multi-layer linear = single linear (collapses). 매 nonlinearity 필수.
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- **Step function in modern NN**: 매 non-differentiable → backprop fail. 매 ReLU/GELU 사용.
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- **Too wide, too shallow**: 매 universal approximation 가능해도 deep 가 sample-efficient.
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- **Forgetting bias**: 매 b=0 forced → cannot shift decision boundary off origin.
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## 🧪 검증 / 중복
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- Verified (Rosenblatt 1958, Minsky-Papert 1969, Rumelhart et al. 1986).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — perceptron history, XOR limit, MLP modern lens |
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