Performance of most flavors of the old generations of learning algorithms will plateau. Deep learning, training large neural networks, is scalable and performance keeps getting better as you feed them more data. — Andrew Ng

Deep learning doesn’t take a huge amount of time or computational resources. Nor does it require highly complex code, and in some cases not even a large amount of training data. Curated best practices are now available as libraries that make it easy to plug in and write your own neural network architectures using a minimal amount of code to achieve more than 90% prediction accuracies.

The two most popular deep learning libraries are: (1) pytorch created by Facebook (we will be using fastai today, which is built on top of pytorch) and (2) the keras-tensorflow framework created by Google.

The Project

We will build an image classifier using the Convolutional Neural Network (CNN) model to predict if a given image is that of Drogon or Vicerion (any Game of Thrones fans here in the house? Clap to say yay!).

You can adapt this problem statement to any type of image classification that interests you. Here are some ideas: cat or dog (classic deep learning 101), if a person is wearing glasses or not, bus or car, hot dog vs not-hot dog (Silicon Valley fans also say yay! ;) ).

Step 1: Installation

You can use any GPU accelerated cloud computing platform for running your model on. For the purpose of this blog we will be using

Once setup, you can launch Jupyter notebook on that machine using the following command:

jupyter notebook

This will give you a localhost URL that you can open in your browser and replace “localhost” with your machine’s IP address to launch your notebook.