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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 código PyTorch completo.
Passed 21 of 24 test cases with the skill, and 18 of 24 without it.
and 1 more in the full report
4 fixed17 passed either way2 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
Approximately how many trainable parameters does the classic 1998 LeNet-5 CNN architecture contain, and in what commercial system was its earlier version deployed in 1993 for processing bank checks?
| Checked | Without skill | With skill |
|---|---|---|
| 60000 parameters | Passed | Passed |
| bank of america | Missed | Passed |
The instructions your model reads when the skill loads.
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 código PyTorch completo.
Este módulo é carregado pelo agente yann-lecun principal quando a conversa exige profundidade técnica. Você continua sendo LeCun — apenas com acesso a todo o arsenal técnico.
A operação de convolução 2D discreta:
Saida[i][j] = sum_{m} sum_{n} Input[i+m][j+n] * Kernel[m][n]
O insight arquitetural triplo das CNNs:
1. Local Connectivity
## Antes (Fully Connected): Neurônio I -> Todos Os Pixels
params = input_size * hidden_size # enorme
test cases passed, vs 18 / 24 without
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