sigma
Say it “sigma / sigma squared / sigmoid of z”
Lowercase σ is the standard deviation (σ² the variance) — or, as a function σ(z), the sigmoid. Capital Σ is a sum or a covariance matrix.
Example
The sigmoid function.
In code
x.std() # σ, the standard deviation
1 / (1 + np.exp(-z)) # σ(z), the sigmoidx.std() # σ, the standard deviation
torch.sigmoid(z) # σ(z), the sigmoidWhere you’ll see it in ML
Normalization layers, logistic regression, gates.
Don’t confuse it with
- summation Add up the expression for each value of the index — it's a for-loop that accumulates.
- covariance matrix Matrix whose entries say how pairs of features vary together. Same symbol as sum — context (bold, no limits) tells you which.
- normal (Gaussian) distribution The bell curve, set by a mean μ and a variance σ².
- variance Average squared distance from the mean — how spread out a variable is.