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% Document Info
\author{khouloudch}
\pdfinfo{
  /Title (neural-nets.pdf)
  /Creator (Cheatography)
  /Author (khouloudch)
  /Subject (Neural Nets Cheat Sheet)
}

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\noindent
\begin{multicols}{3}
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    \vspace{-7pt}
    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
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\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Neural Nets Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{khouloudch} via \textcolor{DarkBackground}{\uline{cheatography.com/71739/cs/18189/}}}
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\noindent
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}khouloudch \\
  \uline{cheatography.com/khouloudch} \\
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\begin{tabulary}{5.8cm}{L}
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Published 7th December, 2018.\\
   Updated 7th December, 2018.\\
   Page {\thepage} of \pageref{LastPage}.
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  Measure your website readability!\\
  www.readability-score.com
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\begin{multicols*}{2}

\begin{tabularx}{8.4cm}{X}
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\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Intro}}  \tn
% Row 0
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\mymulticolumn{1}{x{8.4cm}}{An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.} \tn 
% Row Count 4 (+ 4)
% Row 1
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\mymulticolumn{1}{x{8.4cm}}{The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems.} \tn 
% Row Count 9 (+ 5)
% Row 2
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\mymulticolumn{1}{x{8.4cm}}{ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.} \tn 
% Row Count 16 (+ 7)
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\par\addvspace{1.3em}

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\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Advantages}}  \tn
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\mymulticolumn{1}{x{8.4cm}}{{\bf{Adaptive learning:}} An ability to learn how to do tasks based on the data given for training or initial experience.} \tn 
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% Row 1
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\mymulticolumn{1}{x{8.4cm}}{{\bf{Self-Organisation:}} An ANN can create its own organisation or representation of the information it receives during learning time.} \tn 
% Row Count 6 (+ 3)
% Row 2
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\mymulticolumn{1}{x{8.4cm}}{{\bf{Real Time Operation:}} ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.} \tn 
% Row Count 10 (+ 4)
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\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Neural Model}}  \tn
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\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Neural Net}}  \tn
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\mymulticolumn{1}{p{8.4cm}}{\vspace{1px}\centerline{\includegraphics[width=5.1cm]{/web/www.cheatography.com/public/uploads/khouloudch_1544216259_neural-networks-layers.jpg}}} \tn 
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\begin{tabularx}{8.4cm}{X}
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\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Learning Process}}  \tn
% Row 0
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\mymulticolumn{1}{x{8.4cm}}{{\bf{Activation Function}}} \tn 
% Row Count 1 (+ 1)
% Row 1
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\mymulticolumn{1}{x{8.4cm}}{Each neuron has an activation function that defines the output of the neuron} \tn 
% Row Count 3 (+ 2)
% Row 2
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\mymulticolumn{1}{x{8.4cm}}{The activation function is used to introduce non-linearity in the modeling capabilities of the network} \tn 
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% Row 3
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\mymulticolumn{1}{x{8.4cm}}{} \tn 
% Row Count 6 (+ 0)
% Row 4
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\mymulticolumn{1}{x{8.4cm}}{{\bf{forwardpropagation}}} \tn 
% Row Count 7 (+ 1)
% Row 5
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\mymulticolumn{1}{x{8.4cm}}{occurs when the network is exposed to the training data and these cross the entire neural network for their predictions (labels) to be calculated.} \tn 
% Row Count 10 (+ 3)
% Row 6
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\mymulticolumn{1}{x{8.4cm}}{} \tn 
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\mymulticolumn{1}{x{8.4cm}}{{\bf{loss function}}} \tn 
% Row Count 11 (+ 1)
% Row 8
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\mymulticolumn{1}{x{8.4cm}}{to estimate the loss (or error) and to compare and measure how good/bad our prediction result was in relation to the correct result (remember that we are in a supervised learning environment and we have the label that tells us the expected value). Ideally, we want our cost to be zero} \tn 
% Row Count 17 (+ 6)
% Row 9
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\mymulticolumn{1}{x{8.4cm}}{as the model is being trained, the weights of the interconnections of the neurons will gradually be adjusted until good predictions are obtained.} \tn 
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% Row 12
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\mymulticolumn{1}{x{8.4cm}}{Once the loss has been calculated, this information is propagated backwards.} \tn 
% Row Count 23 (+ 2)
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\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Visual Scheme}}  \tn
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\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{Learning process of one perceptron}}  \tn
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% That's all folks
\end{multicols*}

\end{document}