Neuron

A neuron takes a group of weighted inputs, applies an activation function, and returns an output.

Inputs to a neuron can either be features from a training set or outputs from a previous layer’s neurons. Weights are applied to the inputs as they travel along synapses to reach the neuron. The neuron then applies an activation function to the “sum of weighted inputs” from each incoming synapse and passes the result on to all the neurons in the next layer. #### Synapse Synapses are like roads in a neural network. They connect inputs to neurons, neurons to neurons, and neurons to outputs. In order to get from one neuron to another, you have to travel along the synapse paying the “toll” (weight) along the way. Each connection between two neurons has a unique synapse with a unique weight attached to it. When we talk about updating weights in a network, we’re really talking about adjusting the weights on these synapses. #### Weights Weights are values that control the strength of the connection between two neurons. That is, inputs are typically multiplied by weights, and that defines how much influence the input will have on the output. In other words: when the inputs are transmitted between neurons, the weights are applied to the inputs along with an additional value (the bias) #### Bias Bias terms are additional constants attached to neurons and added to the weighted input before the activation function is applied. Bias terms help models represent patterns that do not necessarily pass through the origin. For example, if all your features were 0, would your output also be zero? Is it possible there is some base value upon which your features have an effect? Bias terms typically accompany weights and must also be learned by your model. #### Layers

- `Input Layer` Holds the data your model will train on. Each neuron in the input layer represents a unique attribute in your dataset (e.g. height, hair color, etc.). - `Hidden Layer` Sits between the input and output layers and applies an activation function before passing on the results. There are often multiple hidden layers in a network. In traditional networks, hidden layers are typically fully-connected layers — each neuron receives input from all the previous layer’s neurons and sends its output to every neuron in the next layer. This contrasts with how convolutional layers work where the neurons send their output to only some of the neurons in the next layer. - `Output Layer` The final layer in a network. It receives input from the previous hidden layer, optionally applies an activation function, and returns an output representing your model’s prediction.