So I created a ton of podcasts. Well, I created a ton of trailers technically. Oh, and websites. And 1 episode.
Anyway, here are all of the show pages:
This is more notes and reference than an in-depth tutorial, but after spending a few hours trying different things, here’s how to get it all set up. Remember, just as Discourse recommends, a t2.micro instance only has 1GB of memory, so if you intend to grow things to an Internet-wide audience, you should use a t2.small instance instead.
As I continue my adventures in machine learning through the FastAI courses, I wanted to explore the concept of dropout rate. If you would like to see the Jupyter Notebook used for these tests, including full annotations about what/why, check out my machine learning github project. Specifically the Testing Dropout Rates (small images).ipynb.
Really quickly, dropout rate is a method in Convolutional Neural Networks (CNNs) of removing neurons (e.g. in the first layer of an image this would be individual pixels) to prevent overfitting (i.e. doing notably better on the training set than on the validation set) and thus increase the general applicability of the model. In other words, block a percentage of the material to force it to not become to overdependent on repeating patterns that lead it astray.
These tests were setup to isolate dropout rate as much as possible. Also, while this test was using ResNet50, results may differ using a different model. Okay, enough jibber-jabber, let’s jump right to the conclusions, shall we?
When getting started exploring machine learning, you will likely come across the free lessons at Fast.ai. These lessons require a few gigabytes worth of programs and algorithms as well as access to a powerful GPU from Nvidia (e.g. GTX 1060). The first lesson even walks you through setting up a cloud server for just that purpose, but what if your PC already has a powerful Nividia graphics card? What if you use Windows?
No problem. This quick guide walks you through the process of setting up a local environment for machine learning, starting with the Fast.ai tutorial series. It’s designed for Windows PCs with an Nvidia graphics card. Alright, let’s get started with a few quick downloads.
After recently acquiring some older windsurfing gear, I was introduced to a lot of old parts and connector systems. Namely, rather than the modern connection systems, a 1990’s era Fanatic board use a U shaped pin to secure the windsurfing board’s female end with the mast’s male end. A stainless steel pin is then slid through existing holes to trap the mast from pulling out. The downside to this system, at least for me, was that the pin was lost while not in use as it’s not permanently attached to either the board or the mast extension. So I built a DIY pin.
This is Part 4 of a series documenting my process during creation and execution of improvised theater.
For the director and producer of a show, work doesn’t start at the beginning of rehearsal, but rather hours, days, or weeks before.
The tool you use is less important than making sure you use it. The free tool pictured above is called Trello, which is a digital version of index cards organized in columns on a pin board (in its simplest form). Whatever you use, these are the columns, or categories, I used for organizing my show:
- Photography and Videography
- Public Relations and Marketing
- Show Production
- Show Elements
This is Part 3 of a series documenting and analyzing the process of producing an original improvised theatre performance.
Rehearsals are ruined by indecision and inaction. Try it, decide, move on.
Rehearsal schedules vary depending on the cast, familiarity with the form or structure, and cast size. This article focuses on an 8, 2-hour rehearsal sessions of a brand new show with a fresh cast. To that end, the rehearsal schedule should be broken into three main phases:
- Discover and finalize the structural elements of the form from beginning to end.
- Practice the form with direct, immediate feedback on the improvisation itself.
- Polish the overall feel of the show as all elements come together.