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!