This repository contains the project files and submissions for Project 2 - Image Classification as part of Udacity's Deep Learning Nanodegree Foundation Program.
This project requires Python 3 and the following Python libraries installed:
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.
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.
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.