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model to provide for comparison and experimentation between different model performances. A Graphical User Interface has been developed to provide a mechanism for a researcher, or interested student, to train and test models and alter key hyper-parameters to explore the effect on performance and results. +\subsection{Overview} + +Developing an Artificial Neural Network has required understanding the fundamental theoretical maths and algorithms underpinning this important technology which has been +exciting and challenging. In implementing the network I have focused on an object-orientated approach utilising design approaches such as encapsulation and suitable data +structures such as doubly linked lists. I have also researched and set up a development environment utilising tools such as GitHub, Jupyter Notebook, Visual Studio Code, +LaTex, automated unit testing etc. At the start of the project, I was lucky enough to interview an expert in the field of Neural Networks and AI and his guidance was valuable +in setting the scope of the project and suggesting properties of the network to test. I am particularly pleased with being able to implement the network from the first +principle maths. The main learning I have taken from this project is that Artificial Neural Networks require tuning to best address a particular problem - they are not a +one-size-fits-all solution - and that this is a combination of research, experimentation and experience. + \pagebreak \end{document} diff --git a/project-report/src/latex/technical-solution.tex b/project-report/src/latex/technical-solution.tex index f1ec9d1..00f1bfa 100644 --- a/project-report/src/latex/technical-solution.tex +++ b/project-report/src/latex/technical-solution.tex @@ -83,7 +83,7 @@ \subsubsection{Organisation} I also utilise a TODO.md file for keeping track of what features and/or bugs need to be worked on. -\subsection{models package} +\subsection{Models package} This package is a self-contained package for creating the trained Artificial Neural Networks and can either be used with a CPU or a GPU, as well as containing the test and training data for all three datasets in a datasets directory. Whilst both the cpu and gpu subpackage are similar in functionality, the cpu subpackage uses NumPy @@ -93,7 +93,7 @@ \subsection{models package} Both the cpu and gpu subpackage contain a utils subpackage that provides the tools for creating Artificial Neural Networks, and three modules that are the implementation of Artificial Neural Networks for each dataset. -\subsubsection{utils subpackage} +\subsubsection{Utils subpackage} \label{sec:utils-subpackage} The utils subpackage consists of a tools.py module that provides a ModelInterface class and helper functions for the model.py module, that contains an AbstractModel @@ -121,7 +121,7 @@ \subsubsection{Artificial Neural Network implementations} \inputminted{python}{./school_project/models/cpu/xor.py} \end{itemize} -\subsection{frames package} +\subsection{Frames package} I have used tkinter for the User Interface and the frames package which consists of tkinter frames to be loaded onto the main window when needed. The package also includes a hyper-parameter-defaults.json file, which stores optimum default values for the hyper-parameters to be set to. @@ -189,7 +189,7 @@ \subsection{frames package} \end{figure} \end{itemize} -\subsection{\_\_main\_\_.py module} +\subsection{Project Entrypoint - \_\_main\_\_.py module} This module is the entrypoint to the project and loads the main window of the User Interface: