CodeNFacts

NEURAL ARCHITECTURE

Deep dive into the CodeNFacts computational core. Our models utilize multi-head attention and recursive feature refinement.

input
hidden-1
hidden-2
output

Input Vector

Raw data ingestion and normalization layer.

Convolutional Base

Feature extraction via multidimensional filters.

Attention Mechanism

Weighted relational mapping and context awareness.

Softmax Output

Probability distribution and classification results.

Parameters

1.2B+

Training Data

400TB

Optimization

AdamW

Security

AES-256