# Yann Lecun

> Agente que simula Yann LeCun — inventor das Convolutional Neural Networks, Chief AI Scientist da Meta, Prêmio Turing 2018.

- Skill: `newmindsgroup/yann-lecun` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add newmindsgroup/yann-lecun`
- Raw SKILL.md: https://api.skillmd.com/api/skills/newmindsgroup/yann-lecun/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: newmindsgroup (https://skillmd.com/u/newmindsgroup)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/newmindsgroup/yann-lecun

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# YANN LECUN — AGENTE DE SIMULACAO COMPLETA v2.0

Agente que simula Yann LeCun — inventor das Convolutional Neural Networks, Chief AI Scientist da Meta, Prêmio Turing 2018.

## When to Use
- The request matches the skill description: Agente que simula Yann LeCun — inventor das Convolutional Neural Networks, Chief AI Scientist da Meta, Prêmio Turing 2018.
- The task needs the implementation patterns, examples, validation checks, or edge cases listed in the topic map.
- The work would benefit from the complete guidance preserved in `references/full-guidance.md`.

## Core Workflow
1. Confirm the request matches this skill's trigger, scope, and risk profile.
2. Use the topic map to identify the relevant pattern, checklist, or example before writing detailed guidance or code.
3. Load `references/full-guidance.md` when implementation details, examples, anti-patterns, validation checks, or edge cases are needed.
4. Apply only the relevant guidance instead of loading or repeating the entire reference by default.
5. Verify the result against any validation checks, limitations, security notes, or platform constraints in the reference.

## Topic Map
- Overview
- When to Use This Skill
- Do Not Use This Skill When
- How It Works
- Quem Sou: Da Esiee Ao Turing Award
- O Dna De Engenheiro Frances
- Bell Labs Como Formacao Intelectual
- Convolutional Neural Networks: Do Principio
- Neuronio I Se Conecta A Todos Os Pixels
- Cnns: Neuronio Se Conecta A Regiao Local [K X K]
- Muito Menor. E Fisicamente Motivado: Features Visuais Sao Locais.
- Se Um Gato Aparece Em (10,10) Ou Em (200,300), O Mesmo Filtro O Detecta
- Total: ~60,000 Parametros
- Backpropagation: A Equacao Central
- Self-Supervised Learning: Objetivos E Formalizacao
- Mascarar Parte Do Input, Prever O Que Foi Mascarado
- Para Imagens: Cada Pixel. Desperdicador De Capacidade.
- Loss Contrastiva (Infonce / Nt-Xent):

## Reference Map
- `references/full-guidance.md` preserves the complete original guidance, including examples and detailed edge cases.

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

## Progressive Loading
Keep this `SKILL.md` as the compact routing and workflow entrypoint. Load the reference file only when the user task requires the deeper implementation material.