diff --git a/diffphys-code-sol.ipynb b/diffphys-code-sol.ipynb
index 3d66715..4c87093 100644
--- a/diffphys-code-sol.ipynb
+++ b/diffphys-code-sol.ipynb
@@ -1357,7 +1357,7 @@
     "err_source = vx_ref - vx_src \n",
     "err_hybrid = vx_ref - vx_hyb \n",
     "v = np.concatenate([err_source,err_hybrid], axis=1)\n",
-    "axes[3].imshow( v , origin='lower', cmap='magma')\n",
+    "axes[3].imshow( v , origin='lower', cmap='cividis')\n",
     "axes[3].set_title(f\" Errors: Source & Learned\")\n",
     "\n",
     "pylab.tight_layout()\n"
diff --git a/make-pdf.sh b/make-pdf.sh
index 0065c3f..fc171b4 100755
--- a/make-pdf.sh
+++ b/make-pdf.sh
@@ -20,9 +20,11 @@ python3.7 json-cleanup-for-pdf.py
 /Users/thuerey/Library/Python/3.7/bin/jupyter-book build . --builder pdflatex
 
 cd _build/latex
-#mv book.pdf book-xetex.pdf # failed anyway
+#mv book.pdf book-xetex.pdf # not necessary, failed anyway
+# this generates book.tex
 
 rm -f book-in.tex sphinxmessages-in.sty book-in.aux book-in.toc
+# rename book.tex -> book-in.tex  (this is the original output!)
 mv book.tex book-in.tex
 mv sphinxmessages.sty sphinxmessages-in.sty
 mv book.aux book-in.aux
@@ -30,9 +32,10 @@ mv book.toc book-in.toc
 #mv sphinxmanual.cls sphinxmanual-in.cls
 
 python3.7 ../../fixup-latex.py
-# generates book-in2.tex
+# reads book-in.tex -> writes book-in2.tex
 
 # remove unicode chars via unix iconv
+# reads book-in2.tex -> writes book.tex
 iconv -c -f utf-8 -t ascii book-in2.tex > book.tex
 
 # finally run pdflatex, now it should work:
diff --git a/overview-equations.md b/overview-equations.md
index 1a76892..044bffe 100644
--- a/overview-equations.md
+++ b/overview-equations.md
@@ -28,7 +28,7 @@ $$ (learn-l2)
 
 We typically optimize, i.e. _train_, 
 with a stochastic gradient descent (SGD) optimizer of choice, e.g. Adam {cite}`kingma2014adam`.
-We'll rely on auto-diff to compute the gradient w.r.t. weights, $\partial f / \partial \theta$,
+We'll rely on auto-diff to compute the gradient of a scalar loss $L$ w.r.t. the weights, $\partial L / \partial \theta$,
 We will also assume that $e$ denotes a _scalar_ error function (also
 called cost, or objective function).
 It is crucial for the efficient calculation of gradients that this function is scalar.