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Blog: Udacity Capstone Project: Machine Learning Engineer Nanodegree


https://github.com/grapestone5321/mlnd-capstone_project/blob/master/project_report.pdf

[Humpback Whale Identification]

Kaggle Competition is used as a technical domain along with the problem and dataset [1].

1 Definition

Project Overview

After centuries of intense whaling, recovering whale populations still have a hard time adapting to warming oceans and struggle to compete every day with the industrial fishing industry for food.

To aid whale conservation efforts, scientists use photo surveillance systems to monitor ocean activity. They use the shape of whales’ tails and unique markings found in footage to identify what species of whale they’re analyzing and meticulously log whale pod dynamics and movements. For the past 40 years, most of this work has been done manually by individual scientists, leaving a huge trove of data untapped and underutilized.

Problem Statement

The challenge is to build an algorithm to identify individual whales in images. Happywhale’s database is analyzed. Happywhale is a platform that uses image process algorithms to let anyone to submit their whale photo and have it automatically identied [2].

The database is over 25,000 images, gathered from research institutions and public contributors. The contributing is helpful to open rich elds of understanding for marine mammal population dynamics around the globe.

The overall strategy for deriving a model architecture was to use convolutional neural networks (CNNs). CNNs have revolutionized the computational pattern recognition process.

References

[1] https://www.kaggle.com/c/humpback-whale-identification.

[2] Happywhale. https://happywhale.com/home.

Source: Artificial Intelligence on Medium

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