add cnn basic infos

src/materialandmethods.tex

@@ -16,6 +16,24 @@
 \subsubsection{ROC and AUC}
 \subsubsection{RESNet}
 \subsubsection{CNN}
+Convolutional neural networks are especially good model architectures for processing images, speech and audio signals.
+A CNN typically consists of Convolutional layers, pooling layers and fully connected layers.
+Convolutional layers are a set of learnable kernels (filters).
+Each filter performs a convolution operation by sliding a window over every pixel of the image.
+On each pixel a dot product creates a feature map.
+Convolutional layers capture features like edges, textures or shapes.
+Pooling layers sample down the feature maps created by the convolutional layers.
+This helps reducing the computational complexity of the overall network and help with overfitting.
+Common pooling layers include average- and max pooling.
+Finally, after some convolution layers the feature map is flattened and passed to a network of fully connected layers to perform a classification or regression task.
+
+\begin{figure}[h]
+    \centering
+    \includegraphics[width=\linewidth]{../rsc/cnn_architecture}
+    \caption{Architecture convolutional neural network. Image by \href{https://cointelegraph.com/explained/what-are-convolutional-neural-networks}{SKY ENGINE AI}}
+    \label{fig:cnn-architecture}
+\end{figure}
+
 \subsubsection{Softmax}
 
 The Softmax function converts $n$ numbers of a vector into a probability distribution.
@@ -24,7 +42,7 @@ Its a generalization of the Sigmoid function and often used as an Activation Lay
 \sigma(\mathbf{z})_j = \frac{e^{z_j}}{\sum_{k=1}^K e^{z_k}} \; for j\coloneqq\{1,\dots,K\}
 \end{equation}
 
-The softmax function has high similarities with the Bolzmann distribution. \cite{Boltzmann}
+The softmax function has high similarities with the Boltzmann distribution and was first introduced in the 19$^{\textrm{th}}$ century~\cite{Boltzmann}.
 \subsubsection{Cross Entropy Loss}
 % todo maybe remove this
 \subsubsection{Adam}