Neuralnet Alphabet

Here contains an alphabet of terms used in neuralnet (neural network):

a - A coefficent, or constant

b - A bias

c - A concatenation in the middle of network

d - Dot product

e - Error or loss

f - Activation function

g - Gradient

h - Hidden layer output

i - Iterator variable

j - Iterator variable

k - Iterator variable

l - Not used, confusing with number 1

m - Number of neurons in a layer

n - Number of layers

o - Not used, confused with number 0

p - Probability P in reinforcement learning

q - Probability Q in reinforcement learning

r - Learning rate

s - Subtraction (u-y) or also called delta

t - Derivative of activation function

u - Output (of feedforward)

v - Backpropagation intermediate value

w - Weight

x - Input

y - True output (or expected output)

z - Latent vector

More terms:

we - A weight on loss node, connected from an output node

fe - Loss function

te - Derivative of loss function

ge - Gradient of loss function

inp - Whole training set of x (input)

exp - Whole training set of y (expected)

wrt - With Respect To

⁠

Want to print your doc?
This is not the way.

Try clicking the ⋯ next to your doc name or using a keyboard shortcut (

CtrlP

) instead.