final

EEMLA.tex

@@ -195,7 +195,7 @@ The attention mechanism serves as a noise filter and integration component, allo
 \newpage
 {\raggedright \normalsize \textbf{Implementation Hardware & Software}}
 
-\section{Implementation in Rust Using Burn}
+
 
 Rust was selected for its memory safety guarantees, zero-cost abstractions, and deterministic concurrency model. The neural network is implemented using the \mintinline{toml}{burn} crate, a modular, backend-agnostic deep learning framework designed for Rust. Burn enables explicit architectural definition via trait-based modules and supports GPU acceleration using backends such as \mintinline{toml}{burn-wgpu} and \mintinline{toml}{burn-candle}. This design aligns with IB Computer Science principles of modularity, abstraction, and system performance.
 
@@ -226,5 +226,7 @@ The \mintinline{toml}{log} crate provides structured runtime logging, while \min
 \printbibliography[heading=none]
 %\printbiblist
 \end{flushleft}
+
+
 \end{document}
 \}