\begin{frame}
	\myheading{Module 11.4 : CNNs (success stories on ImageNet)}
\end{frame}

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\begin{frame}
	\begin{block}{ImageNet Success Stories(roadmap for rest of the talk)}
		\onslide<1-3>{
			\begin{itemize}
				\justifying
				\item <1-> AlexNet
				\item <2-> ZFNet
				\item <3-> VGGNet
			\end{itemize}
		}
	\end{block}
\end{frame}

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\begin{frame}
	%\centering
	\begin{flushleft}
		\input{modules/Module4/tikz_images/cnn_timeline.tex}
	\end{flushleft}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{frame}
	\begin{block}{ImageNet Success Stories(roadmap for rest of the talk)}
		\onslide<1>{
			\begin{itemize}
				\justifying
				\item <1-> \textcolor{red}{AlexNet}
				\item <1-> ZFNet
				\item <1-> VGGNet
				      
			\end{itemize}
		}
	\end{block}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{frame}
	\input{modules/Module4/tikz_images/alex_net.tex}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{frame}
	\begin{columns}
		\column{0.3\textwidth}
		\begin{itemize}
			\justifying
			\item <1-> Let us look at the connections in the fully connected layers in more detail
			\item <2-> We will first stretch out the last conv or maxpool layer to make it a 1d vector
			\item <3-> This 1d vector is then densely connected to other layers just as in a regular feedforward neural network 
		\end{itemize}
		
		\column{0.7\textwidth}
		\begin{overlayarea}{\textwidth}{\textheight}
			\input{modules/Module4/tikz_images/full_conn.tex}
		\end{overlayarea}
	\end{columns}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{frame}
	\begin{block}{ImageNet Success Stories(roadmap for rest of the talk)}
		\onslide<1>{
			\begin{itemize}
				\justifying
				\item <1-> \textcolor{red}{AlexNet}
				\item <1-> \textcolor{red}{ZFNet}
				\item <1-> VGGNet
				      
			\end{itemize}
		}
	\end{block}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{frame}
	\input{modules/Module4/tikz_images/zf_net.tex}
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{frame}
	\begin{block}{ImageNet Success Stories(roadmap for rest of the talk)}
		\onslide<1>{
			\begin{itemize}
				\justifying
				\item <1-> AlexNet
				\item <1-> ZFNet
				\item <1-> \textcolor{red}{VGGNet}
				      
			\end{itemize}
		}
	\end{block}
	
\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\begin{frame}
	\noindent
	 \input{modules/Module4/tikz_images/vgg_net.tex}      
	       
	\begin{itemize}
		\justifying
		\item<24-> Kernel size is $3 \times 3$ throughout
		\item<25-> Total parameters in non FC layers = $\sim16M$
		\item<26->
		\footnotesize{
			           
			\onslide<26->{Total Parameters in FC layers =} \onslide<27->{$\ (512 \times 7 \times 7 \times 4096)$}\onslide<28->{ + $(4096 \times 4096)$} \onslide<29->{ + $(4096 \times 1024)$}\onslide<30->{ = $\sim122M$}
		}
		
		\item<31-> Most parameters are in the first FC layer ($\sim$ 102M)
		       
	\end{itemize}
\end{frame}

