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title: Neural Networks Lecture
categories: lecture
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# Briefing
## What is a newural network
+ The single Neuron
+ Weighted Input
+ Activation
+ The network model
+ Input/Output
+ Weights
+ Activation Function
+ The Tensor Model
## Output and Loss Function
+ Classification versus Regression
**MSE**
$$L = (x-y)^2$$
**CrossEntropy**
$$L = \log \frac{ \exp x_{y} } { \sum \exp x_i }$$