SP19-DL-collaborative-notes/main.tex at master · Atcold/SP19-DL-collaborative-notes · GitHub

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\documentclass[oneside]{book}
\usepackage{float}
\input{preamble}
\input{jupyter}

\title{SP19 DL collaborative notes}
\author{
  The students of SP19 Deep Learning\\
  Editor: Alfredo Canziani\\
  NYU
}
\date{\today}

\begin{document}

\maketitle

\input{preface}
\input{instructions}

\tableofcontents

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% NEW PARTS
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\part{Paradigms}\label{prt:prdg}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\input{prdg/QC/01-a/theory}
\input{prdg/YC/02-a/theory} %backprogation ch 5
\input{prdg/RM/10-a_unsup_learning/unsupervised-learning.tex}
\input{prdg/RM/10-b_unsup_energy/Energy_based_unsupervised_learning.tex}
\input{prdg/QC/13/theory}
\input{prdg/BW/07-b/energy_based_models} % Energy based models ch 16
%\input{prdg/BW/08-a/theory} % Energy based models ch 17
\input{prdg/BA/self-supervised-learning}
%\input{labs/12/theory}
\input{prdg/BA/self_supervised_intro_theory}
\input{prdg/BA/self_supervised_theory}
\input{arch/BA/gnn_theory}
\input{prdg/BA/vae_gan}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\part{Architectures}\label{prt:arch}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\input{arch/KF/convolutional-nets} % ch 7
\input{arch/YC/05-lab/theory} %components of cnn
\input{arch/MG/rnn.tex}

\input{arch/BW/08-b/Latent_Variable_Models} %Latent variable models ch 18
\input{arch/IC/graph-cnn.tex}
\input{arch/GS/convolutions.tex}
\input{arch/GS/convolutions_practice.tex}
\input{arch/GS/striding.tex}
\input{arch/IC/Bayesian_Neural_Networks.tex}
\input{arch/IC//optimization.tex}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\part{Practical \& applications}\label{prt:prct}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\input{prct/YC/02-b/theory} %backprogation in practice ch 6
\input{prct/YC/01-b/practice} %manifold hypo ch 31
\input{prct/YC/01-lab/practice} % tensor transform ch 32
\input{prct/KF/supervised_classification} % ch 33
\input{prct/KF/loss_functions_non-convex} % ch 34
\input{prct/KF/visualizing_2D_interpolations} % ch 36
\input{prct/KF/convolution_demonstration} % ch 37
\input{prct/KF/automatic_differentiation} % ch 38, 39 + random projections
\input{prct/KF/img_classification_comparison}
\input{prct/BW/09-lab/theory} % Regularisation ch 19
\input{prct/BW/09-lab/practice} % Regularisation ch 42
\input{prct/RM/11-a_repr_learning/representation_learning.tex}
\input{prct/RM/11-b_repr_poincarre/Poincarre_embeddings.tex}
\input{prct/RM/12-a_sparse-coding/sparse_coding.tex}
\input{prct/MG/truck-backer-upper.tex} % lab 13
\input{prct/GS/CNN_applications.tex}
\input{prct/GS/CNN_applications_2.tex}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\part{Coding}\label{prt:code}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\input{code/MG/pytorch-and-tensors.tex} % lab 01
\input{code/BW/03-lab/coding} % regression coding ch 48
\input{code/IC/Multimodule_Systems_coding.tex}
\input{code/IC/Sequence_Modeling.tex}
\input{code/BA/ae_coding}
\input{code/BA/vae_coding}
\input{code/BA/gnn_coding}
\input{code/KF/bayesian-nn-coding}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% OLD PARTS
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\part{Theory}\label{prt:theory}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% \input{lectures/01-a/theory}
% \input{lectures/01-b/theory}
\input{arch/EN/hierarchical_representation.tex}
\input{arch/EN/nonlinear_dim_expansion.tex}
\input{arch/EN/modular_approach.tex}
%\input{lectures/02-a/theory}
%\input{lectures/02-b/theory}
\input{arch/KF/convolutional-nets}
\input{arch/PK/convolutional_neural_nets.tex}
%\input{lectures/04-a/theory}
%\input{labs/04/theory}
%\input{labs/05/theory}
\input{arch/PK/digression_fourier_transform.tex}
% \input{lectures/06-b/graph-cnn}
%\input{lectures/06-b/theory-rnn}
%\input{labs/07/theory}

%\input{lectures/07-a/optimization}
%\input{lectures/07-b/energy_based_models}
%\input{lectures/08-a/theory}
%\input{lectures/08-b/Latent_Variable_Models}
%\input{labs/09/theory}


% \input{lectures/10-a/unsupervised-learning}
% \input{lectures/10-b/Energy_based_unsupervised_learning}
% \input{lectures/11-a/representation_learning}
% \input{lectures/12-a/sparse_coding}

% \input{labs/13/theory}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\part{Practice}\label{prt:practice}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%\input{lectures/01-b/practice}
%\input{labs/01/practice}
%\input{prct/KF/supervised_classification}
%\input{prct/KF/loss_functions_non-convex}
%\input{labs/03/practice}
%\input{prct/KF/visualizing_2D_interpolations}
%\input{prct/KF/convolution_demonstration}
%\input{prct/KF/automatic_differentiation}
%\input{prct/KF/img_classification_comparison}
%\input{labs/08/practice}
%\input{labs/09/practice}
%\input{labs/13/practice}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\part{Coding}\label{prt:coding}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% \input{lectures/01-b/coding}
%\input{labs/01/coding}
%\input{labs/03/coding}
%\input{labs/08/coding}


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\part{Applications}\label{prt:apps}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%\input{lectures/04-b/applications}
%\input{lectures/06-a/applications}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\part{Papers summary}\label{prt:papers}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

The end.  % \part needs to be followed by something.

\bibliographystyle{plainnat}
\clearpage\bibliography{references}

\end{document}