Replace \cal with \mathbb (#2637)

Co-authored-by: Yong He <yhe@nvidia.com>

1 files changed, 5 insertions, 5 deletions

diff --git a/docs/user-guide/07-autodiff.md b/docs/user-guide/07-autodiff.md
index 6ab4e6332..7d0624cf3 100644
--- a/docs/user-guide/07-autodiff.md
+++ b/docs/user-guide/07-autodiff.md
@@ -120,20 +120,20 @@ Where $$\mathbf{w}_0$$ is the first layer of parameters.
 ### Forward Propagation of Derivatives
 When developing and training such a system, we are typically interested in evaluating the partial derivative of the system output with regard to some parameter $$\omega$$. To do so we can utilize the forward and backward derivative propagation functions for each $$f_i$$. Where the forward derivative propagation function is defined by:
 
-$$ \cal{F}[f_i] = f_i'(\mathbf{w}_i, \mathbf{w}_i') = \sum_{\omega_i\in\mathbf{w}_i} \frac{\partial f}{\partial \omega_i} \omega_i' $$
+$$ \mathbb{F}[f_i] = f_i'(\mathbf{w}_i, \mathbf{w}_i') = \sum_{\omega_i\in\mathbf{w}_i} \frac{\partial f}{\partial \omega_i} \omega_i' $$
 
 Where $$\omega' \in \mathbf{w}'$$ represents the partial derivative of $$\omega_i$$ with regard to some upstream parameter $$\omega_{i-1}$$ that is used to compute $$\omega_i$$, i.e. $$\omega'=\frac{\partial \omega_{i}}{\partial \omega_{i-1}}$$.
 
-Given this definition, $$\cal{F}[f]$$ can be used as a forward propagation function that is able to compute $$\frac{\partial f_i}{\partial \omega_0}$$ from $$\frac{\partial \omega_{i-1}}{\partial \omega_0}$$.
+Given this definition, $$\mathbb{F}[f]$$ can be used as a forward propagation function that is able to compute $$\frac{\partial f_i}{\partial \omega_0}$$ from $$\frac{\partial \omega_{i-1}}{\partial \omega_0}$$.
 
 ### Backward Propagation of Derivatives
 When training a neural network, we are more interested in figuring out the partial derivative of the final system output with regard to a parameter $$\omega_i$$ in $$f_i$$. To do so, we generally utilize the backward derivative propagation function
 
-$$\cal{B}[f_i] = f_i^{-1}(\frac{\partial Y}{\partial f_i}) = \frac{\partial Y}{\partial \mathbf{w}_i}$$
+$$\mathbb{B}[f_i] = f_i^{-1}(\frac{\partial Y}{\partial f_i}) = \frac{\partial Y}{\partial \mathbf{w}_i}$$
 
-Where the backward propagation function $$\cal{B}[f_i]$$ takes as input the partial derivative of the final system output $$Y$$ with regard to the output of $$f_i$$ (i.e. $$\mathbf{w}_i$$), and computes the partial derivative of the final system output with regard to the input of $$f_i$$ (i.e. $$\mathbf{w}_{i-1}$$).
+Where the backward propagation function $$\mathbb{B}[f_i]$$ takes as input the partial derivative of the final system output $$Y$$ with regard to the output of $$f_i$$ (i.e. $$\mathbf{w}_i$$), and computes the partial derivative of the final system output with regard to the input of $$f_i$$ (i.e. $$\mathbf{w}_{i-1}$$).
 
-The higher order operator $$\cal{F}$$ and $$\cal{B}$$ represent the operations that converts an original or primal function $$f$$ to its forward or backward derivative propagation function. Slang's automatic differentiation feature provide builtin support for these operators to automatically generate the derivative propagation functions from a user defined primal function. The remaining documentation will discuss this feature from a programming language perspective.
+The higher order operator $$\mathbb{F}$$ and $$\mathbb{B}$$ represent the operations that converts an original or primal function $$f$$ to its forward or backward derivative propagation function. Slang's automatic differentiation feature provide builtin support for these operators to automatically generate the derivative propagation functions from a user defined primal function. The remaining documentation will discuss this feature from a programming language perspective.
 
 ## Differentiable Types
 Slang will only generate differentiation code for values that has a *differentiable* type. A type is differentiable if it conforms to the builtin `IDifferentiable` interface. The definition of the `IDifferentiable` interface is: