Commit ded66428 authored by Loïc Barrault's avatar Loïc Barrault
Browse files

TP L6 + changes

parent 62929611
......@@ -94,7 +94,7 @@
%\input{ie_ner.tex} % end of 5th lecture + 6th lecture
%\input{ie_relation_extraction.tex} % 7th lecture
\input{tp_nn.tex}
\input{textproc_nn.tex}
\input{sa_extra_reading}
......
......@@ -44,7 +44,7 @@
Department of Computer Science\\
}
\date{November 26, 2019}
\date{December 3, 2019}
\begin{document}
......
\documentclass[aspectratio=169,t,xcolor=table]{beamer}
%\documentclass[t]{beamer}
%\documentclass[handout,t]{beamer}
\mode<presentation>
{
%\usetheme{PaloAlto}
% \usetheme{Hannover}
\usetheme{informatics}
\useoutertheme{infolines}
% \setbeamercovered{transparent} % or whatever (possibly just delete it)
}
\setbeamertemplate{navigation symbols}{}
\setlength{\extrarowheight}{3pt}
\input{../LatexColors.incl.tex}
\input ../mycolors.tex
\input ../macros.tex
\input ../macros_en.tex
\input ../macros_beamer.tex
\usepackage{setspace}
%\setbeamercovered{transparent}
\usepackage[absolute,showboxes,overlay]{textpos}
%\TPshowboxestrue % commenter une fois fini
\TPshowboxesfalse % décommenter pour faire disparaitre les boites
\textblockorigin{10mm}{10mm} % origine des positions
% This is only inserted into the PDF information catalog. Can be left out.
\subject{COM3110/Text Processing}
\title[]{[Text Processing]\\ Deep Learning}
\author[L. Barrault]{Loïc Barrault}
\institute[University of Sheffield]
{
l.barrault@sheffield.ac.uk \\
Department of Computer Science\\
}
\date{December 3, 2019}
\begin{document}
\begin{frame}
\titlepage
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%\input{textproc_introduction}
\input{textproc_nn.tex}
%\input{extra_reading}
% and kill the abominable icon
\setbeamertemplate{bibliography item}{}
\begin{frame}[allowframebreaks]
\frametitle{References}
% \bibliographystyle{amsalpha}
% \bibliographystyle{apalike}
\bibliographystyle{IEEEtran}
{\footnotesize \bibliography{refs,refs_sa} }
\end{frame}
\end{document}
......@@ -5,7 +5,7 @@
\vfill
\centering
\Huge{\edinred{Text processing\\Deep Learning}}
\Huge{\edinred{[Text processing]\\Deep Learning}}
\end{frame}
......@@ -59,45 +59,51 @@ Hebb: \myemph{``Neurons that fire together, wire together''}
\frametitle{The perceptron}
\textbf{Perceptron}: computing unit loosely inspired by the biological neuron
\vfill
\centerline{
\begin{tabular}[c]{cc}
\begin{tabular}[c]{c}
\includegraphics[width=0.4\textwidth]{figures/BpNeurone}
\end{tabular}
\hspace*{-1cm}
&
\begin{columns}
\begin{column}{.5\textwidth}
\begin{center}
\includegraphics[width=0.6\textwidth]{figures/BpNeurone}
\end{center}
\end{column}
\begin{column}{.5\textwidth}
\begin{center}
\begin{tabular}[c]{rl}
input: & $x_i$ \\
weights: & $w_i$ \\
\textbf{input}: & $\vx = \{x_i\}$ \\
\myemph{weights}: & $\vw = \{w_i\}$ \\
threshold: & $s$ \\
activity: & $\displaystyle a = \sum_i w_i x_i + s$ \\
output: & $y=f(a)$ \\
activation function: & $f=threshold(a)$
\myemph{activation function}: & $f=\text{threshold function}$ \\
\textbf{output}: & $\hat{y}=f(a)$ \\
\end{tabular}
\end{tabular}
}
\vfill
\end{center}
\end{column}
\end{columns}
\vspace{.5cm}
Training method: change the weights $\vw$ if a training example $\vx$ is misclassified as follows:
\begin{itemize}
\item[] $\hat{\vw}^{new} = \hat{\vw}^{cur} + \hat{\vx} . y $ ~~~ with ~~~ $ y \in \{+1, -1\}$
\end{itemize}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}
\frametitle{The Perceptron and the logical functions}
\hspace{1cm}
\begin{tabular}[t]{c}
y = a OR b \\[10pt]
y = a OR b \\[5pt]
\includegraphics[height=0.7\textheight]{figures/or}
\end{tabular}
\hfill
\hspace{1cm}%
\begin{tabular}[t]{c}
y = a AND b \\[10pt]
\includegraphics[height=0.7\textheight]{figures/and}
y = a AND b \\[5pt]
\includegraphics[height=0.69\textheight]{figures/and}
\end{tabular}
\hfill
\hspace{1cm}%
\begin{tabular}[t]{c}
y = a XOR b \\[10pt]
\includegraphics[height=0.7\textheight]{figures/xor}
y = a XOR b \\[5pt]
\includegraphics[height=0.73\textheight]{figures/xor}
\end{tabular}
\end{frame}
......@@ -125,13 +131,27 @@ y_i^{c} & = & f \left(\sum_j w^{c-1}_{ij} ~ y_j^{c-1}\right) \\
\Ra\ \myemph{propagation} of the input $\vx$ towards the output $\vy$
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}
\frametitle{How to train a multilayer perceptron?}
\begin{block}{\center \textbf{Backpropagation}}
\begin{center}
\Large{Backward propagation of errors}
\end{center}
\end{block}
\begin{itemize}
\item What error? \Ra\ Error function depending on the task
\item Estimating a real value: \ra\
\end{itemize}
\end{frame}
......@@ -151,6 +171,15 @@ y_i^{c} & = & f \left(\sum_j w^{c-1}_{ij} ~ y_j^{c-1}\right) \\
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}
\frametitle{TITLE}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}
\frametitle{Deep Learning for Sentiment Analysis}
......
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