Xander Steenbrugge shared his latest work on LinkedIn yesterday, and I was completely stunned!
Xander had been working on, what he called, a “fun side-project”, but which was in my eyes, absolutely awesome. He had used two generative adversarial networks (GANs) to teach one another how to respond visually to changing audio cues.
This resulted in the generation of stunning audio-visual fanatasy worlds that are complete brain porn. You just can’t stop staring. So much is happening in these video’s; everything looks familiar, whereas nothing really represent anything realistic. There’s always a sliver of reality before the visual shapes morph to their next form.
Have a look yourself at the video’s on Xander’s new Youtube channel “Neural Synesthesia“ dedicated to this project. The videos are also hosted here on Vimeo, where they are rendered in higher resolution even.
This is my favorite video, but there are more below.
Amazing how the image responds to changes in the music, right? I suspect Xander let’s the algorithm traverse some latent space with spaces that are determined by the bass, trebble, and other audio-cues.
Here’s another one of Xander’s videos, with the same audio track as background:
But Xander didn’t limit his GANs to generating landscapes and still paintings, but he also dared to do some human faces. These also turned out amazing.
Both the left and right face seem to start out in about the same position/seed in the latent space, but traverse in different, though still similar directions, morphing into all kinds of reaslistic and more alien forms. The result is simply out of this world!
Curious to see where this project and others head as we continue to see development in this GAN field. This must turn the world of design and art up side down in the coming decade…
A beautiful machine-generated still from the Neural Synthesia videos (link)
Sharon’s list contains many neat tricks, some of which less well-known base functions, others features of more niche packages. Here’s the ones I am definitely adding to my R tricks overview and want to highlight here as well:
Categorize values into interval cut()
Convert numbers that came in as strings with commas to R numbers with readr::parse_number(mydf$mycol)
Create a searchable, sortable HTML table in 1 line of code with DT::datatable(mydf, filter = 'top')
Display a fraction between 0 and 1 as a percentage with scales::percent(myfraction)
Generate a vector of 1:length(myvec) with seq_along(myvec)
Now that I’m slowly familiarizing myself in the world of Python, I am much more often confronted with classes and object-oriented programming (OOP). While R has its own OOP paradigms (yes, multiple, obviously, it’s R after all), I have never experienced the need to create my own classes. However, in other languages, like Python, Ruby, or Java, OOP is much more an essential of developers’ and programmers’ skillsets.
Now, I personally won’t start on learning Java anytime soon. Hence, I am just sharing this pearl of a resource with a wider audience right now. This MOOC by the university of Helsinki has been in my inbox for quite a while: Object-Oriented Programming with Java. If you understand Finnish, you can even take the 2019 Finnish version of the course.
During this course you will learn all the basics of computer programming, algorithms and object-oriented programming using the Java programming language. The course includes comprehensive course materials and plenty of programming exercises, each tested using our automatic testing service Test My Code.
Part 1 of the course will teach you all the basics of the Java language:
Part 2 continues with some more advanced topics:
While I have not taken the course myself yet, I have read a lot of good reviews about it. Moreover, what better way to learn a new language than by deep diving into it with a specialized topic like OOP. And it’s free! And taught by trained academics! What are you still doing here, start learning!
Harvard (bio)statisticians Miguel Hernan and Jamie Robins just released their new book, online and accessible for free!
The Causal Inference book provides a cohesive presentation of causal inference, its concepts and its methods. The book is divided in 3 parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from complex longitudinal data. Here’s the official Harvard page for the book release.
This is definitely an interesting read for epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, data scientists, computer scientists, and any other person with a love for proper data analysis!
After several years of proscrastinating, the inevitable finally happened: Three months ago, I committed to learning Python!
I must say that getting started was not easy. One afternoon three months ago, I sat down, motivated to get started. Obviously, the first step was to download and install Python as well as something to write actual Python code. Coming from R, I had expected to be coding in a handy IDE within an hour or so. Oh boy, what was I wrong.
Apparently, there were already a couple of versions of Python present on my computer. And apparently, they were in grave conflict. I had one for the R reticulate package; one had come with Anaconda; another one from messing around with Tensorflow; and some more even. I was getting all kinds of error, warning, and conflict messages already, only 10 minutes in. Nothing I couldn’t handle in the end, but my good spirits had dropped slightly.
With Python installed, the obvious next step was to find the RStudio among the Python IDE’s and get working in that new environment. As an rational consumer, I went online to read about what people recommend as a good IDE. PyCharm seemed to be quite fancy for Data Science. However, what’s this Spyder alternative other people keep talking about? Come again, there are also Rodeo, Thonny, PyDev, and Wing? What about those then? A whole other group of Pythonista’s said that, as I work in Data Science, I should get Anaconda and work solely in Jupyter Notebooks! Okay…? But I want to learn Python to broaden my skills and do more regular software development as well. Maybe I start simple, in a (code) editor? However, here we have Atom, Sublime Text, Vim, and Eclipse? All these decisions. And I personally really dislike making regrettable decisions or committing to something suboptimal. This was already taking much, much longer than the few hours I had planned for setup.
This whole process demotivated so much that I reverted back to programming in R and RStudio the week after. However, I had not given up. Over the course of the week, I brought the selection back to Anaconda Jupyter Notebooks, PyCharm, and Atom, and I was ready to pick one. But wait… What’s this Visual Studio Code (VSC) thing by Microsoft. This looks fancy. And it’s still being developed and expanded. I had already been working in Visual Studio learning C++, and my experiences had been good so far. Moreover, Microsoft seems a reliable software development company, they must be able to build a good IDE? I decided to do one last deepdive.
The more I read about VSC and its features for Python, the more excited I got. Hey, VSC’s Python extension automatically detects Python interpreters, so it solves my conflicts-problem. Linting you say? Never heard of it, but I’ll have it. Okay, able to run notebooks, nice! Easy debugging, testing, and handy snippets… Okay! Machine learning-based IntelliSense autocompletes your Python code – that sounds like something I’d like. A shit-ton of extensions? Yes please! Multi-language support – even tools for R programming? Say no more! I’ll take it. I’ll take it all!
Linting in VSC provides code suggestions
My goods friends at Microsoft were not done yet though. To top it all of, they have documented everything so well. It’s super easy to get started! There are numerous ordered pages dedicated to helping you set up and discover your new Python environment in VSC:
The Microsoft VSC pages also link to some more specific resources:
Editing Python in VS Code: Learn more about how to take advantage of VS Code’s autocomplete and IntelliSense support for Python, including how to customize their behvior… or just turn them off.
Linting Python: Linting is the process of running a program that will analyse code for potential errors. Learn about the different forms of linting support VS Code provides for Python and how to set it up.
Debugging Python: Debugging is the process of identifying and removing errors from a computer program. This article covers how to initialize and configure debugging for Python with VS Code, how to set and validate breakpoints, attach a local script, perform debugging for different app types or on a remote computer, and some basic troubleshooting.
Unit testing Python: Covers some background explaining what unit testing means, an example walkthrough, enabling a test framework, creating and running your tests, debugging tests, and test configuration settings.
Python IntelliSense in VSC makes real-time code autocomplete suggestions
My Own Python Journey
So three months in I am completely blown away at how easy, fun, and versatile the language is. Nearly anything is possible, most of the language is intuitive and straightforward, and there’s a package for anything you can think of. Although I have spent many hours, I am very happy with the results. I did not get this far, this quickly, in any other language. Let me share some of the stuff I’ve done the past three months.
I’ve mainly been building stuff. Some things from scratch, others by tweaking and recycling other people’s code. In my opinion, reusing other people’s code is not necessarily bad, as long as you understand what the code does. Moreover, I’ve combed through lists and lists of build-it-yourself projects to get inspiration for projects and used stuff from my daily work and personal life as further reasons to code. I ended up building:
solutions to the first 31 problems of Project Euler, which I highly recommend you try to solve yourself!
solutions to the first dozen problems posed in Automate the Boring Stuff with Python. This book and online tutorial forces you to get your hands dirty right from the start. Simply amazing content and the learning curve is precisely good
"Programming today is a race between software engineers striving to build bigger and better idiot-proof programs, and the universe trying to build bigger and better idiots. So far, the universe is winning." – Rick Cook#programming#coding#ArtificialStupidity no.20 pic.twitter.com/cBiR1HQszn
all Socratica Python Youtube videos. They are simply a fantastic introduction to the language and amazingly amusing. You can sponsor them here
hours and hours of Corey Shafer’s Youtube channel. Seriously good quality content, and more in-depth than Socratica. Corey covers the versatile functionalities included in the standard Python libraries and then some more
Although it is no longer maintained, you might find some more, interesting links on my Python resources page or here, for those transitioning from R. If only the links to the more up-to-date resources pages. Anyway, hope this current blog helps you on your Python journey or to get Python and Visual Studio Code working on your computer. Please feel free to share any of the stories, struggles, or successes you experience!
In this awesome 8-minute read, R-progidy Colin Fay explains in laymen’s terms what Docker images, Docker containers, and Volumes are; what Rocker is; and how to set up a Docker container with an R image and run code on it:
On your machine, you’re going to need two things: images, and containers. Images are the definition of the OS, while the containers are the actual running instances of the images. […] To compare with R, this is the same principle as installing vs loading a package: a package is to be downloaded once, while it has to be launched every time you need it. And a package can be launched in several R sessions at the same time easily.
paulvanderlaken.com 