Udacity: Deep Learning Nanodegree - Project 2

#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.

##Instructions

Login to your AWS instance Download the repo git clone https://github.com/udacity/deep-learning.git Change to the project directory cd deep-learning/image-classification/ Enter your deep learning environment Mac/Linux: source activate dl Windows: activate dl Run the notebook jupyter notebook dlnd_image_classification.ipynb Go to the instance (x.x.x.x:8888) in your web browser The x.x.x.x is your instance's ip address Follow the instructions in the notebook will lead you through the project. Ensure you've passed the unit tests in the notebook before you submit the project! 

##Submission

Ensure you've passed all the unit tests in the notebook. Ensure you pass all points on the rubric.

When you're done with the project, please save the notebook as an HTML file. You can do this by going to the File menu in the notebook and choosing "Download as" > HTML. Ensure you submit both the Jupyter Notebook and it's HTML version together. Package the "dlnd_image_classification.ipynb", "helper.py", "problem_unittests.py", and the HTML file into a zip archive, or push the files from your GitHub repo.

Hit Submit Project below!