Awhile ago, I had AWS set up to provide me a unique URL that I could navigate to and use jupyter notebooks. I admired the convenience and the ability to just start a computation and close my laptop knowing full well my computations continued working away. However, using an AWS P2 instance can get very costly depending on your usage, which for me would be around $600 per month. So, I figured I could just build a computer with that kind of money which could serve as a deep learning rig along with the occasional video gaming :).
Vim is an amazing text editor and over the years has allowed me to be far more efficient when writing code or editing text in general. Although the initial learning curve is a bit steep, it’s well worth the time to learn to navigate and edit files without your mouse. But what makes Vim even more powerful is that it’s hackable - if you find yourself executing a sequence of keystrokes over and over for certain tasks, you can create your own function that can be used throughout Vim.
I know, trying to set up a deep learning environment on Windows isn’t exactly the first thought you have when trying to get GPU options working for Keras and Theano any time you have a machine learning or data science problem. Setting up something like this on Ubuntu would be easier, no doubt, but if you prefer Windows or don’t feel like dual booting every time you need to do some number crunching, then the following instructions will help you to install and configure Keras, Theano, pygpu, and cuDNN on Windows 10.
Below is a collection of links that might be useful for a developer. It’s in its infancy at the moment and will be updated over time.
Below is a compilation of command line tips that have helped me over the years. As this is a cheat sheet, it will be updated over time to include new cheats and to maintain existing ones.