Can this skill help with PyTorch implementation?

Yes, it includes full PyTorch pseudocode and implementation patterns for target encoders, momentum updates, and specific loss functions like NT-Xent.

Does it handle technical math and gradients?

Yes, it covers the core equations for backpropagation, convolution operations, and the mathematical formulation of energy functions.

What is the difference between AMI and LLMs according to this skill?

The skill explains how AMI (Advanced Machinery of Intelligence) focuses on world models, planning, and objective-driven behavior rather than simple token prediction.

What specific architectures does this skill cover?

It provides deep coverage of CNNs, LeNet, I-JEPA, V-JEPA, MC-JEPA, SimCLR, MAE, BYOL, and Energy-Based Models (EBMs).

Yann LeCun Technical AI Expert

Name: Yann LeCun Technical AI Expert
Author: sickn33

bysickn33

•

35,079

•

数据科学与机器学习

Provides deep technical implementation and theoretical guidance for Yann LeCun's research, including JEPA, CNNs, and Energy-Based Models.

This specialized skill equips Claude with the technical depth required to implement and explain Yann LeCun's most influential AI research. It focuses on non-generative architectures like the Joint-Embedding Predictive Architecture (JEPA), Energy-Based Models (EBMs), and Self-Supervised Learning (SSL) frameworks. Ideal for researchers and machine learning engineers, it provides high-fidelity PyTorch code, mathematical formulations for backpropagation and convolutions, and architectural insights for building Advanced Machinery of Intelligence (AMI). This skill bridges the gap between high-level research concepts and production-grade implementation.

主要功能

01Complete PyTorch implementations for I-JEPA, V-JEPA, and SimCLR

02Detailed frameworks for Energy-Based Models (EBMs) and contrastive loss

0335,079 GitHub stars

04Architectural blueprints for Autonomous Machine Intelligence (AMI) systems

05Deep-dive explanations of CNN translation equivariance and weight sharing

06Comparative analysis of World Models versus Large Language Models

使用场景

01Researching alternatives to pixel-based generative modeling

02Designing hierarchical predictive architectures for autonomous agents

03Building self-supervised computer vision pipelines in PyTorch

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sickn33/antigravity-awesome-skills yann-lecun-tecnico

For use in Claude.ai and ChatGPT

主要功能

01Complete PyTorch implementations for I-JEPA, V-JEPA, and SimCLR

02Detailed frameworks for Energy-Based Models (EBMs) and contrastive loss

0335,079 GitHub stars

04Architectural blueprints for Autonomous Machine Intelligence (AMI) systems

05Deep-dive explanations of CNN translation equivariance and weight sharing

06Comparative analysis of World Models versus Large Language Models

使用场景

01Researching alternatives to pixel-based generative modeling

02Designing hierarchical predictive architectures for autonomous agents

03Building self-supervised computer vision pipelines in PyTorch

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sickn33/antigravity-awesome-skills yann-lecun-tecnico

For use in Claude.ai and ChatGPT