1 files changed, 3 insertions, 3 deletions

diff --git a/doc/tex/results.tex b/doc/tex/results.tex
index 3c586e4..9c1e52c 100644
--- a/doc/tex/results.tex
+++ b/doc/tex/results.tex
@@ -36,7 +36,7 @@ The Monte Carlo Algorithm tries to explore the tree of possibilities as
 efficiently as possible. With this approach, it can be expected to fail when
 alone on a problem such big as the game of Go. Nonetheless, there are some areas
 where it can be useful. It will be evaluated by its capabilities while playing
-games but also when presented with go problems.
+games but also when presented with Go problems.
 
 The Monte Carlo algorithm has been set to do 5 explorations with 10 simulations
 each when it is asked for a move. In the hardware used this makes it think for
@@ -77,7 +77,7 @@ the board, for which the player must find some beneficial move. Life and death
 problems are a subset of tsumegos in which the survival of a group depends on
 finding the correct sequence to save or kill the group. One collection of such
 tsumegos is \textit{Cho Chikun's Encyclopedia of Life and Death}, part of which
-are available on OGS\cite{ogsLifeAndDeath}, an online go server.
+are available on OGS\cite{ogsLifeAndDeath}, an online Go server.
 
 The first of these problems and what the algorithm suggested as moves is shown
 in \fref{fig:mctsProblem01}.
@@ -184,7 +184,7 @@ move), can be seen on Figs.~\ref{fig:denseVSdense01}, \ref{fig:denseVSdense02},
 
 The dense network starts on the center of the board, which is one of the
 standard openings in the 9x9 board. It starts on a very good track, but we must
-acknowledge that the empty board is a position present on every go match it has
+acknowledge that the empty board is a position present on every Go match it has
 trained on and so it should know it well. It probably means the center was the
 most played opening in the sample. It is interesting to check the heatmap of
 this move, since the selected move has only a score of 0.27. Other common