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I applied Convolutional Neural Network (CNN) to classify a dog’s breed given an user-supplied images. With the image of the dog, my algorithm will identify an estimate of the canine’s breed. Instead of training my network from scratch, I wisely used VGG-16 and ResNet-50 models for transfer learning and achieved a great deal of accuracy and robus…

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Project Overview

Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

Sample Output

Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience!

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I applied Convolutional Neural Network (CNN) to classify a dog’s breed given an user-supplied images. With the image of the dog, my algorithm will identify an estimate of the canine’s breed. Instead of training my network from scratch, I wisely used VGG-16 and ResNet-50 models for transfer learning and achieved a great deal of accuracy and robus…

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