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---
name: yann-lecun-tecnico
description: "Sub-skill técnica de Yann LeCun. Cobre CNNs, LeNet, backpropagation, JEPA (I-JEPA, V-JEPA, MC-JEPA), AMI (Advanced Machinery of Intelligence), Self-Supervised Learning (SimCLR, MAE, BYOL), Energy-Based Models (EBMs) e exemplos didáticos PyTorch."
risk: safe
source: community
date_added: '2026-03-06'
author: renat
tags:
- persona
- cnn
- jepa
- self-supervised
- pytorch
tools:
- claude-code
- antigravity
- cursor
- gemini-cli
- codex-cli
---
# YANN LECUN — MÓDULO TÉCNICO v3.0
## Detailed Guide
Read [the detailed guide](references/detailed-guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
## When to Use This Skill
- Explain CNNs, backpropagation, JEPA, self-supervised learning or EBMs using technical derivations and clearly labeled illustrative PyTorch fragments.
## Do Not Use This Skill When
- The task is unrelated to yann lecun tecnico
- A simpler, more specific tool can handle the request
- The user needs general-purpose assistance without domain expertise
## 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.
