/image_classification

In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset will need to be preprocessed, then train a convolutional neural network on all the samples. You'll normalize the images, one-hot encode the labels, build a convolutional layer, max pool layer, and fully connected layer. At then end, you'll see their predictions on the sample images.

Primary LanguageJupyter Notebook

Image Classification

Introduction

In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset will need to be preprocessed, then train a convolutional neural network on all the samples. You'll normalize the images, one-hot encode the labels, build a convolutional layer, max pool layer, and fully connected layer. At then end, you'll see their predictions on the sample images.

Getting the project files

The project files can be found in our public GitHub repo, in the image-classification folder. You can download the files from there, but it's better to clone the repository to your computer

This way you can stay up to date with any changes we make by pulling the changes to your local repository with git pull. Please note this project is written in Python 3.x.

Starting the Project

For this assignment, you can find the image_classification folder containing the necessary project files on the Machine Learning projects GitHub, under the projects folder. You may download all of the files for projects we'll use in this Nanodegree program directly from this repo. Please make sure that you use the most recent version of project files when completing a project!

This project contains 3 files:

  • image_classification.ipynb: This is the main file where you will be performing your work on the project.
  • Two helper files, problem_unittests.py and helper.py