The first lesson gives an introduction into the why and how of the fast.ai course, and you will learn the basics of Jupyter Notebooks and how to use the fast.ai library to build a world-class image classifier in three lines of Python.
It seems I am spending more and more of my days in Jupyter Notebooks lately. While still following the fast.ai course - sometimes life gets in the way of your plans - I noticed datasets are either included or there is a link to a .zip file, which you still need to download and extract by hand. After some manual repetitions, I started dabbling with a small script to make that part easier, so I could just focus on running experiments in the future.
While completing the Coursera Deep Learning specialization, I started to wonder whether it would be possible to run the Jupyter Notebooks from the exercises on your local machine (or on a different server for that matter).
4 March 2018
Updated the table of contents to reflect latest updates:
-Split Datasets and models into separate sections
-Added a “Python Libraries” section for non-DL specific, yet useful Python libs”
This post appeared first on LinkedIn on 15 February 2018.