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\author{lwebzem56}
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  /Title (neural-networks-for-machine-learning.pdf)
  /Creator (Cheatography)
  /Author (lwebzem56)
  /Subject (Neural Networks for Machine Learning Cheat Sheet)
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    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Neural Networks for Machine Learning Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{lwebzem56} via \textcolor{DarkBackground}{\uline{cheatography.com/51511/cs/14084/}}}
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}lwebzem56 \\
  \uline{cheatography.com/lwebzem56} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Published 28th December, 2017.\\
   Updated 1st January, 2018.\\
   Page {\thepage} of \pageref{LastPage}.
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  Measure your website readability!\\
  www.readability-score.com
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\begin{multicols*}{3}

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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Neural Networks Types and Main Features}}  \tn
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Feedforward neural network & connections between nodes do not have a cycle \tn 
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Multilayer perceptron (MLP) & has at least three layers of nodes \tn 
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Reccurent neural network (RNN) & connections between units have a directed cycle \tn 
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Self-Organising Maps (SOM) & convert input data to low dimensional space \tn 
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Deep Belief Network (DBN) & has connections between layers but not within layer \tn 
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Convolutional Neural Network (CNN) & has one or more convolutional layers and then followed by one or more fully connected layers \tn 
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Generative Adversarial Networks (GAN) & system of two neural nets, contesting with each other \tn 
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Spiking Neural Netorks (SNN) & time information is processed in the form of spikes and there is more than one synapse between neurons \tn 
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Wavelet neural network & use wavelet function as activation function in the neuron \tn 
% Row Count 31 (+ 3)
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Neural Networks Types and Main Features (cont)}}  \tn
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Wavelet convolutional neural network & combine wavelet transform and CNN \tn 
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Long short-term memory (LSTM) & type of RNN,  model for the short-term memory which can last for a long period of time \tn 
% Row Count 7 (+ 5)
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\par\addvspace{1.3em}

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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Building Neural Network with Keras and Python}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{from keras.models import Sequential \newline model = Sequential() \newline from keras.layers import Dense \newline  \newline model.add(Dense(units=64, activation='relu', input\_dim=100)) \newline model.add(Dense(units=10, activation='softmax')) \newline  \newline model.compile(loss='categorical\_crossentropy', \newline               optimizer='sgd', \newline               metrics={[}'accuracy'{]}) \newline model.compile(loss=keras.losses.categorical\_crossentropy, \newline               \seqsplit{optimizer=keras.optimizers.SGD(lr=0.01}, momentum=0.9, nesterov=True)) \newline  \newline model.fit(x\_train, y\_train, epochs=5, batch\_size=32) \newline  \newline model.train\_on\_batch(x\_batch, y\_batch) \newline loss\_and\_metrics = model.evaluate(x\_test, y\_test, batch\_size=128) \newline classes = model.predict(x\_test, batch\_size=128)} \tn 
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\mymulticolumn{1}{x{5.377cm}}{\{\{popup="https://keras.io/"\}\}Keras\{\{/popup\}\}}  \tn 
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Data Preparation for Input to Neural Network}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{from sklearn import preprocessing \newline  \newline def normalize\_data(m, XData): \newline     if m == "": \newline         m="scaling-no"   \newline     if m == "scaling-no": \newline         return XData \newline     if m ==  "StandardScaler": \newline         std\_scale = \seqsplit{preprocessing.StandardScaler().fit(XData)} \newline         XData\_new = \seqsplit{std\_scale.transform(XData)} \newline     if m == "MinMaxScaler": \newline         minmax\_scale = \seqsplit{preprocessing.MinMaxScaler().fit(XData)} \newline         XData\_new = \seqsplit{minmax\_scale.transform(XData)} \newline  \newline     return XData\_new} \tn 
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\begin{tabularx}{5.377cm}{X}
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Cheat Sheets about Python and Machine Learning}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{\{\{popup="http://intelligentonlinetools.com/cheatsheets/python\_data\_manipulation.php"\}\}Quick and Easy Way to get started with common and most used python tasks in data processing\{\{/popup\}\}} \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Neural Network Applications and Most Used Networks}}  \tn
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Image classification & CNN \tn 
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Image recognition & CNN \tn 
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Time series prediction & RNN, LSTM \tn 
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Text generation & RNN, LSTM \tn 
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Classification & MLP \tn 
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Visualization & SOM \tn 
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\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{Neural Net Weight Update Methods}}  \tn
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Adam & based  on  adaptive  estimates  of  lower order  moments \tn 
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AdaGrad & Adagrad is an adaptive learning rate method \tn 
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RMSProp & adaptive learning rate method, modification of Adagrad method \tn 
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SGD & Stochastic gradient descent \tn 
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AdaDelta & modification of Adagrad to reduce its aggressive, monotonically decreasing learning rate \tn 
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Newton method & second order method, is not used in deep learning \tn 
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Momentum & method that helps accelerate SGD in the relevant direction \tn 
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Nesterov accelerated gradient & evaluate the gradient at  next position instead of current \tn 
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\mymulticolumn{2}{x{5.377cm}}{References: \newline \{\{popup="https://arxiv.org/pdf/1412.6980.pdf"\}\}ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION\{\{/popup\}\} \newline \{\{popup="http://cs231n.github.io/neural-networks-3/\#sgd"\}\}Convolutional Neural Networks for Visual Recognition.\{\{/popup\}\} \newline \{\{popup="http://ruder.io/optimizing-gradient-descent/index.html\#adadelta"\}\}An overview of gradient descent optimization algorithms\{\{/popup\}\} \newline \{\{popup="https://en.wikipedia.org/wiki/Artificial\_neural\_network"\}\}Wikipedia -Artificial neural network \{\{/popup\}\}}  \tn 
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Links}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{\{\{popup="http://www.intelligentonlinetools.com/cgi-bin/nn/neural\_networks\_with\_python.cgi"\}\} Neural Networks with Python on the Web \{\{/popup\}\}} \tn 
% Row Count 3 (+ 3)
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\mymulticolumn{1}{x{5.377cm}}{\{\{popup="https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/"\}\}Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras\{\{/popup\}\}} \tn 
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\mymulticolumn{1}{x{5.377cm}}{\{\{popup="http://www.nehalemlabs.net/prototype/blog/2013/10/10/implementing-a-recurrent-neural-network-in-python/"\}\}Implementing a recurrent neural network in python\{\{/popup\}\}} \tn 
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\mymulticolumn{1}{x{5.377cm}}{\{\{popup="http://intelligentonlinetools.com/blog/2017/06/17/time-series-prediction-with-convolutional-neural-networks-and-keras/"\}\}Time Series Prediction with Convolutional Neural Networks and Keras\{\{/popup\}\}} \tn 
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% That's all folks
\end{multicols*}

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