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NEURAL NETWORKS APPLICATIONS IN VALUATION OF BANNER AD CREATIVE EFFICIENCY

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1500 руб.
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Теория
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74 лист.
Год написания

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Работа пользователя Vseznayka1995
Добрый день! Уважаемые студенты, Вашему вниманию представляется дипломная работа на тему: «NEURAL NETWORKS APPLICATIONS IN VALUATION OF BANNER AD CREATIVE EFFICIENCY »
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Table of Contents

Abstract 3
1.    Introduction  5
2.    Literature review   10
3.    Theoretical framework  19
3.1 Neural networks  19
3.2 Data augmentation   26
3.3 Visualizing convolutional neural networks  40
4.    The application of neural networks in creative advertising strategies  52
5.    Results of computational experiments  57
6.    Conclusion  69
Bibliography  72
Additional materials  76


Abstract
               
This work explores the application of convolutional neural networks to advertisement banners, trying to predict whether the banners have a higher than average click-through rate or less than that. The data used in this work was sourced from Mediascope (company) internet monitoring as advertisement banners and their respective click-through rates. The topic of this work was motivated by a particular problem during the research phase in advertising campaign planning.
This work consists of a brief introduction to the topic, as well as the problem addressed in this work and laying the ground for the solution provided.
Then it is followed by a literature review section, which explores the application of neural networks in image classification, as well as techniques for improving model results.
The literature review is a ground for a theoretical framework, which briefly discusses methods and techniques which are important for building an accurate model and avoid overfitting.
Following the very technical parts of this work, in the solution section a real-life application is discussed, which actually motivated the topic of this work. The problem for which the solution is aimed is briefly discussed, together with the advantages and disadvantages of the proposed solution.
Finally, the results of the experiment, which came in a form of convolutional neural network model, are being discussed together with applied techniques, assumptions, and limitations. The visualizations are created to show how the model decides on the classification of an image.
And in conclusion we sum up the results of the work while talking about the application of the solution provided in this work in a new business era.


Bibliography
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