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
