Swag (#68)

* abstract

* Add style cleanup

* Extra swag

sections/abstract.tex

@@ -1,6 +1,6 @@
 Current deep convolutional networks are fixed in their topology.
 
-We explore the possibilites of making the convolutional topology a parameter itself by combining NeuroEvolution of Augenting Topologies (NEAT) with Convolutional Neural Networks (CNNs) and propose such a system using blocks of Residual Networks (ResNets).\\
-We then explain how such a system can only be built once additional optimizations have been made, as genetic algorithms are way more demanding than training per Backpropagation.
+We explore the possibilites of making the convolutional topology a parameter itself by combining NeuroEvolution of Augmenting Topologies (NEAT) with Convolutional Neural Networks (CNNs) and propose such a system using blocks of Residual Networks (ResNets).\\
+We then explain how our suggested system can only be built once additional optimizations have been made, as genetic algorithms are way more demanding than training per backpropagation.
 
 On the way there we explain most of those buzzwords and offer a gentle and brief introduction to the most important modern areas of machine learning.

sections/introduction to neural networks/what is a neural network.tex

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-A neural network follows the “divide and conquer” principle. It consists of two simple components: neurons and connections.
+The most famous neural network is you. Or, in other words, the human brain.\\
+It is, simply put, a clever arrangement of smallest units capable of processing easy logic.\\
+These smallest units are called \textbf{neurons}, and our brain consists of approximately 100 billion of them.\\
+They are interweaved through a complex series of incomming and outgoing extensions called dendrites and axons, respectively, of which some transport electricity faster than others. Most of the components of a brain are unfortunately still not understood well enough to be used productively in computer science.
+
+An \textbf{artificial neural network} (ANN) tries to emulate the immense success of its biological counterpart by abstracting the complex chemical reactions responsible for our thoughts to much more graspable math.\\
+The feedforward version of such an ANN consists of two simple components: neurons and connections.
 
 Each neuron has inputs, which are the incoming connections. It applies a simple mathematical operation to this set of inputs and returns the result.