asrdav / cifar10-image-classification

This repository contains the project files and submissions for Project 2 - Image Classification as part of Udacity's Deep Learning Nanodegree Foundation Program.

CIFAR10-Image-Classification

Udacity Deep Learning Nanodegree Foundation

Install

This project requires Python 3 and the following Python libraries installed:

• NumPy
• Tensorflow

You will also need to have software installed to run and execute a Jupyter Notebook

If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included.

Run

In a terminal or command window, navigate to the top-level project directory (that contains this README) and run one of the following commands:

ipython notebook dlnd_image_classification.ipynb

or

jupyter notebook dlnd_image_classification.ipynb

This will open the Jupyter Notebook software and project file in your browser.

About

In this project, we will classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, we will see the CNN's predictions on the sample images.