Notation¶
Functions¶
| Symbol | Meaning |
|---|---|
| Sum | |
| Product | |
| Sigmoid function | |
| Expectation | |
| Natural logarithm | |
| Absolute value of x |
Sets¶
| Symbol | Meaning |
|---|---|
| The set of real numbers | |
| The set of vectors of real numbers of length |
|
| The set of matrices of real numbers of size |
Calculus¶
| Symbol | Meaning |
|---|---|
| Derivative of f, shorthand for |
|
| Second derivative of f | |
| Derivative of y with respect to x | |
| Second derivative of y with respect to x | |
| Partial derivative of y with respect to x | |
| Second partial derivative of y with respect to x |
Information theory¶
| Symbol | Meaning |
|---|---|
| KL-divergence between two distributions, P and Q |
Linear algebra¶
| Symbol | Meaning |
|---|---|
| A vector | |
| A matrix | |
| Transpose of X | |
| Conjugate transpose of X | |
| Inverse of X | |
| Euclidean norm of x | |
| Identity matrix | |
| Element-wise product of X and Y | |
| Kronecker product of X and Y | |
| Dot product of x and y | |
| Trace of X | |
| Determinant of X |
Probability¶
| Symbol | Meaning |
|---|---|
| A random variable | |
| Probability of a particular value of X. Shorthand for |
|
| Uniform distribution | |
| Normal distribution |
Statistics¶
| Symbol | Meaning |
|---|---|
| Mean | |
| Standard deviation | |
| Variance of X | |
| Covariance of X and Y |
Machine learning¶
| Symbol | Meaning |
|---|---|
| Parameters of the model | |
| Observations or data | |
| Feature vector | |
| Loss function | |
| Label | |
| Prediction | |
| Gradient at time t | |
| Parameter update at time t | |
| Learning rate |
Reinforcement learning¶
| Symbol | Meaning |
|---|---|
| Policy | |
| Action at time t | |
| State at time t | |
| Reward at time t | |
| Value function | |
| Action set | |
| Discount factor |