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Diffstat (limited to 'chapters/background/01-neural-networks.tex')
| -rw-r--r-- | chapters/background/01-neural-networks.tex | 4 |
1 files changed, 2 insertions, 2 deletions
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] |