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\title{Deep Learning for NLP}
\subtitle{A Gentle Introduction}
\author{Jane Researcher}
\institute{University of Example}
\date{\today}

\begin{document}

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  \titlepage
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  \tableofcontents
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\section{Introduction}

\begin{frame}{What is Deep Learning?}
  Deep learning trains multi-layer neural networks to learn
  hierarchical representations from data.

  \bigskip
  \textbf{Key building blocks:}
  \begin{itemize}
    \item<1-> Linear layers: $\mathbf{y} = W\mathbf{x} + \mathbf{b}$
    \item<2-> Activation functions: ReLU, sigmoid, GELU
    \item<3-> Optimisation: stochastic gradient descent
  \end{itemize}
\end{frame}

% ---- Section 2 ----
\section{Attention Mechanism}

\begin{frame}{Self-Attention}
  Attention maps each token to a weighted sum of all others:
  \begin{equation}
    \text{Attention}(Q, K, V)
      = \text{softmax}\!\left(\frac{QK^\top}{\sqrt{d_k}}\right) V
  \end{equation}

  \pause

  \begin{block}{Complexity}
    Standard self-attention is $\mathcal{O}(n^2)$ in sequence length $n$.
  \end{block}
\end{frame}

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\section{Results}

\begin{frame}{Benchmark Results}
  \begin{center}
    \begin{tabular}{lcc}
      \toprule
      Model        & BLEU & ROUGE-L \\
      \midrule
      Baseline     & 28.4 & 41.2 \\
      Transformer  & 41.0 & 52.3 \\
      \textbf{Ours} & \textbf{44.2} & \textbf{55.1} \\
      \bottomrule
    \end{tabular}
  \end{center}

  \only<2>{
    \begin{alertblock}{Key Takeaway}
      Our model improves BLEU by 3.2 points over the transformer baseline.
    \end{alertblock}
  }
\end{frame}

% ---- Conclusion ----
\begin{frame}{Conclusion}
  \begin{itemize}
    \item Introduced a sub-quadratic attention mechanism
    \item Demonstrated gains on BLEU and ROUGE benchmarks
    \item Code available at \texttt{github.com/example/repo}
  \end{itemize}

  \bigskip
  \centering
  \Large\textbf{Thank you!}\\[6pt]
  \normalsize\texttt{[email protected]}
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