ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
Datashader is a graphics pipeline system for creating meaningful representations of large datasets quickly and flexibly. Datashader breaks the creation of images into a series of explicit steps that allow computations to be done on intermediate representations. This approach allows accurate and effective visualizations to be produced automatically without trial-and-error parameter tuning, and also makes it simple for data scientists to focus on particular data and relationships of interest in a principled way.
This last year I’ve been getting back into machine learning and AI, rediscovering the things that drew me to it in the first place. I’m still in the “learning” and “small studies” phase that naturally precedes crafting any new artwork, and I wanted to share some of that process here. This is a fairly linear record of my path, but my hope is that this post is modular enough that anyone interested in a specific part can skip ahead and find something that gets them excited, too. I’ll cover some experiments with these general topics: Convolutional Neural Networks, Recurrent Neural Networks, Dimensionality Reduction and Visualization, Autoencoders
via https://medium.com/@kcimc/a-return-to-machine-learning–2de3728558eb
I’ve spent many years referencing Wikipedia’s list of cognitive biases whenever I have a hunch that a certain type of thinking is an official bias but I can’t recall the name or details. It’s been an invaluable reference for helping me identify the hidden flaws in my own thinking. Nothing else I’ve come across seems to be both as comprehensive and as succinct.
However, honestly, the Wikipedia page is a bit of a tangled mess. Despite trying to absorb the information of this page many times over the years, very little of it seems to stick. I often scan it and feel like I’m not able to find the bias I’m looking for, and then quickly forget what I’ve learned. I think this has to do with how the page has organically evolved over the years. Today, it groups 175 biases into vague categories (decision-making biases, social biases, memory errors, etc) that don’t really feel mutually exclusive to me, and then lists them alphabetically within categories. There are duplicates a-plenty, and many similar biases with different names, scattered willy-nilly.
I’ve taken some time over the last four weeks (I’m on paternity leave) to try to more deeply absorb and understand this list, and to try to come up with a simpler, clearer organizing structure to hang these biases off of.
https://betterhumans.coach.me/cognitive-bias-cheat-sheet-55a472476b18#.4ksyv6992
This is the first part of a few blog posts on this topic. Apologies ahead of time if you don’t find the topic of visualizing the 24 hours of the day as fascinating as I do, but I’m going to take the time to fully geek out and focus in on this very specific problem in depth. This is Part 1: Explaining the Challenge and Reviewing the Status Quo. This is sort of like a lit review; it’s my attempt to consolidate everything I can find about how people are currently representing 24-hour cyclical data.
http://dougmccune.com/blog/2011/04/21/visualizing-cyclical-time-hour-of-day-charts/