\begin{frame}
	\myheading{Module 16.2: Applications of Encoder Decoder models}
\end{frame}

\begin{frame}
\only<1->{\begin{block}{}
      \begin{itemize}
          \justifying
            \item<1-> For all these applications we will try to answer the following questions
            \item<2-> What kind of a network can we use to encode the input(s)? (What is an appropriate encoder?)
            \item<3-> What kind of a network can we use to decode the output? (What is an appropriate decoder?)
            \item<4-> What are the parameters of the model ?
            \item<5-> What is an appropriate loss function ?
        \end{itemize}
        \end{block}
        }

\end{frame}


\begin{frame}
  \begin{columns}
    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
	\vspace{0.3cm}
  \centering

	\scalebox{0.8}{
		\input{modules/Module2/tikz_images/rnn8.tex}
}
      \end{overlayarea}

    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}

		\footnotesize{
\begin{itemize}

        	\justifying
        		\item<1-> \textbf{Task: } Image captioning
            \item<2-> \textbf{Data:} $\{x_i=image_i,\ y_i=caption_i\}_{i=1}^{N}$
            \item<3-> \textbf{Model:}
            \footnotesize{\begin{itemize}
              \item<4-> \textbf{Encoder:}
              \vspace{-0.2cm}
                \begin{align*}
                s_0 = CNN(x_i)
                \end{align*}
                \vspace{-0.6cm}
                \item<5-> \textbf{Decoder:}
                \vspace{-0.2cm}
                \begin{align*}
                  s_t &= RNN(s_{t-1}, e(\hat{y}_{t - 1}))\\
                  P(y_t|y_1^{t-1}, I) &= softmax(Vs_t + b)
                \end{align*}
            \end{itemize}}
            \vspace{-0.7cm}
            \item<6-> \textbf{Parameters:} $U_{dec}$, $V$, $W_{dec}$, $W_{conv}, b$
            \item<7-> \textbf{Loss:}
            \vspace{-0.2cm}
              \begin{align*}
            \mathscr{L}(\theta) &= \sum \limits _{i=1}^T \mathscr{L}_t(\theta)
            &=-\sum \limits _{t=1}^T \log P(y_t=\ell_t|y_1^{t-1},I)
            \end{align*}
            \item<8-> \textbf{Algorithm:} Gradient descent with backpropagation
        \end{itemize}

}
      \end{overlayarea}
  \end{columns}
\end{frame}


\begin{frame}
  \begin{columns}
    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
	\centering
	\only<1-16>{		\input{modules/Module2/tikz_images/option1.tex}
}
  %\only<8>{\input{option2}}

      \end{overlayarea}

    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}


               \footnotesize{ \begin{itemize}

          \justifying
        		\item<1-> \textbf{Task: } Textual entailment
            \item<2-> \textbf{Data:} $\{x_i=premise_i,\ y_i=hypothesis_i\}_{i=1}^{N}$
            \item<3-> \textbf{Model (Option 1):}
            \footnotesize{\begin{itemize}
              \item<4-> \textbf{Encoder:}
              \vspace{-0.2cm}
                \begin{align*}
                h_t &= RNN(h_{t-1}, x_{it}) \\
                \end{align*}
                \vspace{-1.3cm}

                \item<5-> \textbf{Decoder:}
                \vspace{-0.2cm}
                \begin{align*}
                  s_0 &= h_T \quad \textit{\scriptsize{(T is length of input)}}\\
                  s_t &= RNN(s_{t-1}, e(\hat{y}_{t - 1}))\\
                  P(y_t|y_1^{t-1}, x) &= softmax(Vs_t + b)
                \end{align*}
            \end{itemize}}
            \vspace{-0.7cm}
            \item<6-> \textbf{Parameters:} $U_{dec}$, $V$, $W_{dec}$, $U_{enc}$, $W_{enc}, b$
            \item<7-> \textbf{Loss:}
            \vspace{-0.2cm}
              \begin{align*}
            \mathscr{L}(\theta) &= \sum \limits _{i=1}^T \mathscr{L}_t(\theta)
            =-\sum \limits _{t=1}^T \log P(y_t=\ell_t|y_1^{t-1}, x)
            \end{align*}
            \vspace{-0.4cm}
            \item<8-> \textbf{Algorithm:} Gradient descent with backpropagation
        \end{itemize}}
      \end{overlayarea}
  \end{columns}
\end{frame}


\begin{frame}
  \begin{columns}
    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
  \centering
  %\only<1-7>{\input{option1}}
\input{modules/Module2/tikz_images/option2.tex}
      \end{overlayarea}

    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
        % \begingroup
        \begin{itemize}
          \justifying
          \footnotesize
        		\item<1-> \textbf{Task: } Textual entailment
            \item<2-> \textbf{Data:} $\{x_i=premise_i,\ y_i=hypothesis_i\}_{i=1}^{N}$
            \item<3-> \textbf{Model (Option 2):}
            \footnotesize{\begin{itemize}
              \item<4-> \textbf{Encoder:}
              \vspace{-0.2cm}
                \begin{align*}
                h_t &= RNN(h_{t-1}, x_{it}) \\
                \end{align*}
                \vspace{-1.3cm}

                \item<5-> \textbf{Decoder:}
                \vspace{-0.2cm}
                \begin{align*}
                  s_0 &= h_T \quad \textit{\scriptsize{(T is length of input)}}\\
                  s_t &= RNN(s_{t-1}, [h_T, e(\hat{y}_{t - 1})])\\
                  P(y_t|y_1^{t-1}, x) &= softmax(Vs_t + b)
                \end{align*}
            \end{itemize}}
            \vspace{-0.7cm}
            \item<6-> \textbf{Parameters:} $U_{dec}$, $V$, $W_{dec}$, $U_{enc}$, $W_{enc}, b$
            \item<7-> \textbf{Loss:}
            \vspace{-0.2cm}
              \begin{align*}
            \mathscr{L}(\theta) &= \sum \limits _{i=1}^T \mathscr{L}_t(\theta)
            =-\sum \limits _{t=1}^T \log P(y_t=\ell_t|y_1^{t-1}, x)
            \end{align*}
            \vspace{-0.4cm}
            \item<8-> \textbf{Algorithm:} Gradient descent with backpropagation
        \end{itemize}
        % \endgroup
      \end{overlayarea}
  \end{columns}
\end{frame}


\begin{frame}
  \begin{columns}
    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
	\centering
\input{modules/Module2/tikz_images/translation.tex}
      \end{overlayarea}

    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}

               \footnotesize{ \begin{itemize}

          \justifying
        	    \item<1-> \textbf{Task: } Machine translation
            \item<2-> \textbf{Data:} $\{x_i=source_i,\ y_i=target_i\}_{i=1}^{N}$
            \item<3-> \textbf{Model (Option 1):}
            \footnotesize{\begin{itemize}
              \item<4-> \textbf{Encoder:}
              \vspace{-0.2cm}
                \begin{align*}
                h_t &= RNN(h_{t-1}, x_{it}) \\
                \end{align*}
                \vspace{-1.3cm}

                \item<5-> \textbf{Decoder:}
                \vspace{-0.2cm}
                \begin{align*}
                  s_0 &= h_T \quad \textit{\scriptsize{(T is length of input)}}\\
                  s_t &= RNN(s_{t-1}, e(\hat{y}_{t - 1}))\\
                  P(y_t|y_1^{t-1}, x) &= softmax(Vs_t + b)
                \end{align*}
            \end{itemize}}
            \vspace{-0.7cm}
            \item<6-> \textbf{Parameters:} $U_{dec}$, $V$, $W_{dec}$, $U_{enc}$, $W_{enc}, b$
            \item<7-> \textbf{Loss:}
            \vspace{-0.2cm}
              \begin{align*}
            \mathscr{L}(\theta) &= \sum \limits _{i=1}^T \mathscr{L}_t(\theta)
            =-\sum \limits _{t=1}^T \log P(y_t=\ell_t|y_1^{t-1}, x)
            \end{align*}
             \vspace{-0.4cm}

            \item<8-> \textbf{Algorithm:} Gradient descent with backpropagation
        \end{itemize}}
              \end{overlayarea}
  \end{columns}
\end{frame}

\begin{frame}
  \begin{columns}
    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
	\centering

\input{modules/Module2/tikz_images/translation_opt2.tex}
      \end{overlayarea}

    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}

               \footnotesize{ \begin{itemize}

          \justifying
        	    \item<1-> \textbf{Task: } Machine translation
            \item<1-> \textbf{Data:} $\{x_i=source_i,\ y_i=target_i\}_{i=1}^{N}$
            \item<1-> \textbf{Model (Option 2):}
            \footnotesize{\begin{itemize}
              \item<1-> \textbf{Encoder:}
              \vspace{-0.2cm}
                \begin{align*}
                h_t &= RNN(h_{t-1}, x_{it}) \\
                \end{align*}
                \vspace{-1.3cm}

                \item<1-> \textbf{Decoder:}
                \vspace{-0.2cm}
                \begin{align*}
                  s_0 &= h_T \quad \textit{\scriptsize{(T is length of input)}}\\
                  s_t &= RNN(s_{t-1}, [h_T, e(\hat{y}_{t - 1})])\\
                  P(y_t|y_1^{t-1}, x) &= softmax(Vs_t + b)
                \end{align*}
            \end{itemize}}
            \vspace{-0.7cm}
            \item<1-> \textbf{Parameters:} $U_{dec}$, $V$, $W_{dec}$, $U_{enc}$, $W_{enc}, b$
            \item<1-> \textbf{Loss:}
            \vspace{-0.2cm}
              \begin{align*}
            \mathscr{L}(\theta) &= \sum \limits _{i=1}^T \mathscr{L}_t(\theta)
            =-\sum \limits _{t=1}^T \log P(y_t=\ell_t|y_1^{t-1}, x)
            \end{align*}
             \vspace{-0.4cm}
            \item<1-> \textbf{Algorithm:} Gradient descent with backpropagation
        \end{itemize}}
              \end{overlayarea}
  \end{columns}
\end{frame}


\begin{frame}
  \begin{columns}
    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
	\centering
	 \input{modules/Module2/tikz_images/transliteration1} %Figure F
      \end{overlayarea}

    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}

               \footnotesize{ \begin{itemize}
          \justifying
            \item<1-> \textbf{Task: } Transliteration
            \item<2-> \textbf{Data:} $\{x_i=srcword_i,\ y_i=tgtword_i\}_{i=1}^{N}$
            \item<3-> \textbf{Model (Option 1):}
            \footnotesize{\begin{itemize}
              \item<4-> \textbf{Encoder:}
              \vspace{-0.2cm}
                \begin{align*}
                h_t &= RNN(h_{t-1}, x_{it}) \\
                \end{align*}
                \vspace{-1.3cm}

                \item<5-> \textbf{Decoder:}
                \vspace{-0.2cm}
                \begin{align*}
                  s_0 &= h_T \quad \textit{\scriptsize{(T is length of input)}}\\
                  s_t &= RNN(s_{t-1}, e(\hat{y}_{t - 1}))\\
                  P(y_t|y_1^{t-1}, x) &= softmax(Vs_t + b)
                \end{align*}
            \end{itemize}}
            \vspace{-0.7cm}
            \item<6-> \textbf{Parameters:} $U_{dec}$, $V$, $W_{dec}$, $U_{enc}$, $W_{enc}, b$
            \item<7-> \textbf{Loss:}
            \vspace{-0.2cm}
              \begin{align*}
            \mathscr{L}(\theta) &= \sum \limits _{i=1}^T \mathscr{L}_t(\theta)
            =-\sum \limits _{t=1}^T \log P(y_t=\ell_t|y_1^{t-1}, x)
            \end{align*}
   			 \vspace{-0.4cm}

            \item<8-> \textbf{Algorithm:} Gradient descent with backpropagation
        \end{itemize}}
      \end{overlayarea}
  \end{columns}
\end{frame}

\begin{frame}
  \begin{columns}
    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
	\centering
	\input{modules/Module2/tikz_images/transliteration_opt2} %Figure F to be drawn
      \end{overlayarea}

    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}

               \footnotesize{ \begin{itemize}

          \justifying
            \item<1-> \textbf{Task: } Transliteration
            \item<1-> \textbf{Data:} $\{x_i=srcword_i,\ y_i=tgtword_i\}_{i=1}^{N}$
            \item<1-> \textbf{Model (Option 2):}
            \footnotesize{\begin{itemize}
              \item<1-> \textbf{Encoder:}
              \vspace{-0.2cm}
                \begin{align*}
                h_t &= RNN(h_{t-1}, x_{it}) \\
                \end{align*}
                \vspace{-1.3cm}

                \item<1-> \textbf{Decoder:}
                \vspace{-0.2cm}
                \begin{align*}
                  s_0 &= h_T \quad \textit{\scriptsize{(T is length of input)}}\\
                  s_t &= RNN(s_{t-1}, [e(\hat{y}_{t - 1}), h_T])\\
                  P(y_t|y_1^{t-1}, x) &= softmax(Vs_t + b)
                \end{align*}
            \end{itemize}}
            \vspace{-0.7cm}
            \item<1-> \textbf{Parameters:}  $U_{dec}$, $V$, $W_{dec}$, $U_{enc}$, $W_{enc}, b$
            \item<1-> \textbf{Loss:}
            \vspace{-0.2cm}
              \begin{align*}
            \mathscr{L}(\theta) &= \sum \limits _{i=1}^T \mathscr{L}_t(\theta)
            =-\sum \limits _{t=1}^T \log P(y_t=\ell_t|y_1^{t-1}, x)
            \end{align*}
             \vspace{-0.4cm}

            \item<1-> \textbf{Algorithm:} Gradient descent with backpropagation
        \end{itemize}}      \end{overlayarea}
  \end{columns}
\end{frame}

\begin{frame}
  \begin{columns}
    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
	\centering
	\input{modules/Module2/tikz_images/qa.tex}
 %Figure G
      \end{overlayarea}

    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}

               \footnotesize{ \begin{itemize}

          \justifying
            \item<1-> \textbf{Task: }  Image Question Answeing
            \item<2-> \textbf{Data:} $\{x_i=\{I, q\}_i,\ y_i=Answer_i\}_{i=1}^{N}$
            \item<3-> \textbf{Model:}
            \footnotesize{\begin{itemize}
              \item<4-> \textbf{Encoder:}
              \vspace{-0.2cm}
                \begin{align*}
                \hat{h}_I &= CNN(I),\ \tilde{h}_t = RNN(\tilde{h}_{t-1}, q_{it})\\
                s &= [\tilde{h}_T;\hat{h}_I]
                \end{align*}
                \vspace{-0.6cm}

                \item<5-> \textbf{Decoder:}
                \vspace{-0.2cm}
                \begin{align*}
                  P(y|q, I) &= softmax(Vs + b)
                \end{align*}
            \end{itemize}}
            \vspace{-0.7cm}
            \item<6-> \textbf{Parameters:} $V$, $b$, $U_{q}$, $W_{q}$, $W_{conv}, b$
            \item<7-> \textbf{Loss:}
            \vspace{-0.2cm}
              \begin{align*}
            \mathscr{L}(\theta) &=  - \log P(y = \ell|I, q)
            \end{align*}
            \item<8-> \textbf{Algorithm:} Gradient descent with backpropagation
        \end{itemize}}    \end{overlayarea}
  \end{columns}
\end{frame}

\begin{frame}
  \begin{columns}
    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
	\centering
	\input{modules/Module2/tikz_images/doc_sum.tex}
 %Figure H
      \end{overlayarea}

    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}

               \footnotesize{ \begin{itemize}

          \justifying
            \item<1-> \textbf{Task: }  Document Summarization
            \item<2-> \textbf{Data:} $\{x_i=Document_i,\ y_i=Summary_i\}_{i=1}^{N}$
            \item<3-> \textbf{Model:}
            \footnotesize{\begin{itemize}
              \item<4-> \textbf{Encoder:}
              \vspace{-0.2cm}
                \begin{align*}
                h_t = RNN(h_{t-1}, x_{it})\\
                \end{align*}
                \vspace{-1.3cm}

                \item<5-> \textbf{Decoder:}
                \vspace{-0.2cm}
                \begin{align*}
                  s_0 &= h_T\\
                  s_t &= RNN(s_{t-1}, e(\hat{y}_{t - 1}))\\
                  P(y_t|y_1^{t-1}, x) &= softmax(Vs_t + b)
                \end{align*}
            \end{itemize}}
            \vspace{-0.7cm}
            \item<6-> \textbf{Parameters:} $U_{dec}$, $V$, $W_{dec}$, $U_{enc}$, $W_{enc}, b$
            \item<7-> \textbf{Loss:}
            \vspace{-0.2cm}
              \begin{align*}
            \mathscr{L}(\theta) &= \sum \limits _{i=1}^T \mathscr{L}_t(\theta)
            =-\sum \limits _{t=1}^T \log P(y_t=\ell_t|y_1^{t-1}, x)
            \end{align*}
                \vspace{-0.4cm}
            \item<8-> \textbf{Algorithm:} Gradient descent with backpropagation
        \end{itemize}}

      \end{overlayarea}
  \end{columns}
\end{frame}
%%
%\begin{frame}
%  \begin{columns}
%    \column{0.5\textwidth}
%      \begin{overlayarea}{\textwidth}{\textheight}
%	\centering
%	\input{modules/Module2/tikz_images/doc_sum_opt2} %Figure H
%      \end{overlayarea}
%
%    \column{0.5\textwidth}
%      \begin{overlayarea}{\textwidth}{\textheight}
%	\begin{itemize}
%        	\justifying
%            \item<1-> \textbf{Task}: Document Summarization
%            \item<1-> \textbf{Input}: Document
%            \item<1-> \textbf{Output}: Summary
%            \item<1-> \textbf{Encoder(s)}: RNN
%            \item<1-> \textbf{Decoder}: RNN
%            \item<1-> \textbf{Loss}:
%            \item<1-> \textbf{Parameters}:
%        \end{itemize}
%
%      \end{overlayarea}
%  \end{columns}
%\end{frame}
%
\begin{frame}
  \begin{columns}
    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
	\centering
		\input{modules/Module2/tikz_images/video_desc.tex}
 %Figure I
      \end{overlayarea}

    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}

               \footnotesize{ \begin{itemize}

          \justifying
            \item<1-> \textbf{Task: }  Video Captioning
            \item<2-> \textbf{Data:} $\{x_i=video_i,\ y_i=desc_i\}_{i=1}^{N}$
            \item<3-> \textbf{Model:}
            \footnotesize{\begin{itemize}
              \item<4-> \textbf{Encoder:}
              \vspace{-0.2cm}
                \begin{align*}
                h_t &= RNN (h_{t-1}, CNN(x_{it}))\\
                \end{align*}
                \vspace{-1.3cm}

                \item<5-> \textbf{Decoder:}
                \vspace{-0.2cm}
                \begin{align*}
	              s_0 &= h_T\\
                  s_t &= RNN(s_{t-1}, e(\hat{y}_{t - 1}))\\
                  P(y_t|y_1^{t-1}, x) &= softmax(Vs_t + b)
                \end{align*}
            \end{itemize}}
            \vspace{-0.7cm}
            \item<6-> \textbf{Parameters:} $U_{dec}$, $W_{dec}$, $V$, $b$, $W_{conv}$, $U_{enc}$, $W_{enc}, b$
            \item<7-> \textbf{Loss:}
            \vspace{-0.2cm}
              \begin{align*}
            \mathscr{L}(\theta) &= \sum \limits _{i=1}^T \mathscr{L}_t(\theta)
            =-\sum \limits_{t=1}^T \log P(y_t=\ell_t|y_1^{t-1}, x)
            \end{align*}
            \vspace{-0.4cm}
            \item<8-> \textbf{Algorithm:} Gradient descent with backpropagation
        \end{itemize}}

      \end{overlayarea}
  \end{columns}
\end{frame}

\begin{frame}
  \begin{columns}
    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
  \centering
  	\input{modules/Module2/tikz_images/surya_nam.tex}
 %Figure K
      \end{overlayarea}

    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}

               \footnotesize{ \begin{itemize}

          \justifying
            \item<1-> \textbf{Task: }  Video Classification
            \item<2-> \textbf{Data:} $\{x_i=Video_i,\ y_i=Activity_i\}_{i=1}^{N}$
            \item<3-> \textbf{Model:}
            \footnotesize{\begin{itemize}
              \item<4-> \textbf{Encoder:}
              \vspace{-0.2cm}
                \begin{align*}
                h_t &= RNN (h_{t-1}, CNN(x_{it}))\\
                \end{align*}
                \vspace{-1.3cm}

                \item<5-> \textbf{Decoder:}
                \vspace{-0.2cm}
                \begin{align*}
                  s &= h_T\\
                  P(y|I) &= softmax(Vs + b)
                \end{align*}
            \end{itemize}}
            \vspace{-0.7cm}
            \item<6-> \textbf{Parameters:} $V$, $b$, $W_{conv}$, $U_{enc}$, $W_{enc}, b$
            \item<7-> \textbf{Loss:}
            \vspace{-0.2cm}
              \begin{align*}
            \mathscr{L}(\theta) &=  - \log P(y = \ell|Video)
            \end{align*}
            \vspace{-0.4cm}
            \item<8-> \textbf{Algorithm:} Gradient descent with backpropagation
        \end{itemize}}
        \end{overlayarea}
  \end{columns}
\end{frame}


\begin{frame}
  \begin{columns}
    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
  \centering
  	\input{modules/Module2/tikz_images/qa1.tex}
%Figure J
      \end{overlayarea}

    \column{0.5\textwidth}
      \begin{overlayarea}{\textwidth}{\textheight}
      \footnotesize{ \begin{itemize}

          \justifying
            \item<1-> \textbf{Task: } Dialog
            \item<2-> \textbf{Data:} $\{x_i=Utterance_i,\ y_i=Response_i\}_{i=1}^{N}$
            \item<3-> \textbf{Model:}
            \footnotesize{\begin{itemize}
              \item<4-> \textbf{Encoder:}
              \vspace{-0.2cm}
                \begin{align*}
                h_t = RNN(h_{t-1}, x_{it}) \\
                \end{align*}
                \vspace{-1.3cm}

                \item<5-> \textbf{Decoder:}
                \vspace{-0.2cm}
                \begin{align*}
                  s_0 &= h_T \quad \textit{\scriptsize{(T is length of input)}}\\
                  s_t &= RNN(s_{t-1}, e(\hat{y}_{t - 1}))\\
                  P(y_t|y_1^{t-1}, x) &= softmax(Vs_t + b)
                \end{align*}
            \end{itemize}}
            \vspace{-0.7cm}
            \item<6-> \textbf{Parameters:} $U_{dec}$, $V$, $W_{dec}$, $U_{enc}$, $W_{enc}$, $b$
            \item<7-> \textbf{Loss:}
            \vspace{-0.2cm}
              \begin{align*}
            \mathscr{L}(\theta) &= \sum \limits _{i=1}^T \mathscr{L}_t(\theta)=-\sum \limits _{t=1}^T \log P(y_t=\ell_t|y_1^{t-1}, x)
            \end{align*}
            \vspace{-0.5cm}
            \item<8-> \textbf{Algorithm:} Gradient descent with backpropagation
        \end{itemize}}

      \end{overlayarea}
  \end{columns}
\end{frame}

\begin{frame}
\begin{overlayarea}{\textwidth}{\textheight}
  \begin{block}{}
  \begin{itemize}
          \justifying
  \item<1-> And the list continues ...
  \item<2-> Try picking a problem from your domain and see if you can model it using the encoder decoder paradigm
  \item<3-> Encoder decoder models can be made even more expressive by adding an ``attention'' mechanism
  \item<4-> We will first  motivate the need for this and then explain how to model it
  \end{itemize}
  \end{block}
\end{overlayarea}
\end{frame}
