From a8bb7736e2e86963bd5761cc05079447abeeaba6 Mon Sep 17 00:00:00 2001
From: ericmarin <maarin.eric@gmail.com>
Date: Tue, 23 Jun 2026 15:26:01 +0200
Subject: using official template + fixing language errors

---
 chapters/background/01-neural-networks.tex | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'chapters/background/01-neural-networks.tex')

diff --git a/chapters/background/01-neural-networks.tex b/chapters/background/01-neural-networks.tex
index dc3e2d4..5eed7bd 100644
--- a/chapters/background/01-neural-networks.tex
+++ b/chapters/background/01-neural-networks.tex
@@ -3,7 +3,7 @@
 
 A neural network is a computational model inspired by biological neural networks. It consists of
 connected nodes called neurons, introduced in its early form by Rosenblatt~\cite{rosenblatt1958perceptron}.
-The \textit{Multi-Layer Perceptrons} is an architecture composed of sequential layers, trained using
+The \textit{Multi-Layer Perceptron} is an architecture composed of sequential layers, trained using
 backpropagation as popularized by Rumelhart et al.~\cite{rumelhart1986learning}.
 
 \subsection{Neuron}
@@ -57,7 +57,7 @@ The output $y$ is defined as:
 
 \subsection{Multi-Layer Perceptron}
 A \textit{Multi-Layer Perceptron} (MLP), illustrated in \textbf{\Cref{fig:mlp}}, is a type of neural network organized
-in fully connected layers of neurons: an input layer, one or more hidden layers and an output layer.
+in fully connected layers of neurons: an input layer, one or more hidden layers, and an output layer.
 An MLP is \textit{feedforward}, meaning that the flow of information is strictly propagated from the input to
 the outputs.
 \begin{figure}[H]
-- 
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