---
title: Neural Networks Lecture
categories: lecture
---

# 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**

$$L = \log \frac{ \exp x_{y} } { \sum \exp x_i }$$