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\pdf_title "Convolutional Neural Networks with DIGITS"
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\pdf_subject "EEEM063 Image Processing & Deep Learning"
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Convolutional Neural Networks with DIGITS
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Andy Pack
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EEEM063
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May 2021
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Department of Electrical and Electronic Engineering
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Faculty of Engineering and Physical Sciences
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University of Surrey
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Andy Pack / 6420013
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May 2021
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EEEM063 Coursework
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Introduction
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Investigations Scope
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Dataset
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Dataset Processing
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Mean shift/whitening/augmentation
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Meta-Parameters
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Epochs/learning rate/momentum?
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Network Architectures
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Convolutional Layers
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Fully-Connected Layers
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The final two layers of AlexNet are dense or fully-connected layers and
 provide two key features in image classification.
 The first is flattening the 2D cross-section of the preceding convolutional
 layers into a 1D representation for propagation to a final one-hot vector
 output.
 The second is as a traditional multi-layer perceptron classifier, taking
 the high-level visual insights of the later convolutional layers and reasoning
 these into a final classification.
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Non-Linearity
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Results
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Discussion
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Conclusions
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