more updates

Author: zingale

content/11-machine-learning/neural-net-derivation.md

@@ -13,8 +13,8 @@ Let's start with our cost function:
 $$\mathcal{L}(A_{ij}) = \sum_{i=1}^{N_\mathrm{out}} (z_i - y_i^k)^2 = \sum_{i=1}^{N_\mathrm{out}}
   \Biggl [ g\biggl (\underbrace{\sum_{j=1}^{N_\mathrm{in}} A_{ij} x^k_j}_{\equiv \alpha_i} \biggr ) - y^k_i \Biggr ]^2$$
 
-where we'll refer to the product ${\boldsymbol \alpha} \equiv {\bf
-Ax}$ to help simplify notation.  This means that ${\bf z} = g({\boldsymbol \alpha})$.
+where we'll refer to the product $\boldsymbol{\alpha} \equiv {\bf
+Ax}$ to help simplify notation.  This means that ${\bf z} = g(\boldsymbol{\alpha})$.
 
 We can compute the derivative with respect to a single matrix
 element, $A_{pq}$ by applying the chain rule:

content/_config.yml

@@ -4,7 +4,7 @@
 title: "PHY 546: Python for Scientific Computing"
 author: <a href="https://zingale.github.io">Michael Zingale</a>
 #logo: logo.png
-copyright: "2022"
+copyright: "2022-2026"
 
 # Force re-execution of notebooks on each build.
 # See https://jupyterbook.org/content/execute.html
@@ -30,7 +30,7 @@ html:
   use_repository_button: true
   extra_footer: |
     <p>
-    &copy; 2023-2026; CC-BY-NC-SA 4.0
+    &copy; 2022-2026; CC-BY-NC-SA 4.0
     <p>
 
 sphinx: