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Deep Learning Methods for Filter Extraction
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Deep Learning Methods for Filter Extraction

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– Deep Learning Methods for Filter Extraction –

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Abstract

With the exponential growth of the information technology, nowadays tremendous amounts of data including images, audio, text and videos, up to millions or billions, are collected for training machine learning models. Deep neural networks (DNNs) are one of the widely used methods today.

Large companies in the uses these methods to recommends buyers with products, filter junk email or text-based hate speeches, understand and translate major languages in real time, and so on.

Inspired by the trend, our work is dedicated to developing and training a deep neural network to extract meaningful patterns from a set of labelled data i.e. making generalizations.

We show that DNNs can learn feature representations that can be successfully applied in a wide spectrum of application domains.

We show how DNNs are applied to classification problems – grading of fresh tomato fruits based on their physical qualities using supervised learning approach. 

Introduction

1.1 Background of the Study

Artificial Intelligence (AI) is transitioning from being our daily helper to something much more powerful – and disruptive – as the new machines are rapidly outperforming the most talented of us in many endeavours (Frank, Roehrig, & Pring, 2017).

The branch of AI concerned with the study and design of computer programs that automatically improve with experience is called machine learning or ML.

The applications of machine continue to explode in the recent years in the field of agriculture, ecommerce, health, banking, news, robotics, transportation, weather forecasts, software, industries and many more.

YouTube uses ML algorithms to suggest videos, Facebook fills newsfeeds, Netflix recommends films and Google search auto-completes texts. We probably interact with machines frequently.

They are used in many of the software programs that we use, such as Microsoft’s infamous (and long abandoned) paperclip in Office (maybe not the most positive example), spam filters, voice recognition software, and lots of computer games (Marsland, 2015).

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