> For the complete documentation index, see [llms.txt](https://lauradang.gitbook.io/notes/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lauradang.gitbook.io/notes/machine-learning/neural-networks/introduction-to-neural-networks.md).

# Introduction To Neural Networks

[MNIST Jupyter Notebook](http://localhost:8888/notebooks/ML/MNIST.ipynb)

### Goal of Network

* Have an input that is going to be mapped to an output
* eg. Input: X1, X2 -> Dog, Cat (Two Neurons)

### How do we achieve this goal?

#### Neural Network Overview

* Through hidden layers with neurons
* Each input will map to each neuron in hidden layer
* Connection between input and neuron have its own unique weight
* One hidden layer between input and output is linear relationship (not complex enough for most cases)

![alt text](https://github.com/lauradang/wiki-notes/blob/master/Users/lauradang/Desktop/neuralnetwork.png)

#### Neurons

* Summed together
* If summation meets certain condition, neuron is fired/activated (spike in segway function - returns value between 0-1)
* Each neuron output will be a segway function between 0-1 (entire layer will add up to 1)

![alt text](https://github.com/lauradang/wiki-notes/blob/master/Users/lauradang/Desktop/neurons.png)