First, Raymond discusses chunking and aliasing. He brings up the theory that the human mind can only handle/remember 7 pieces of information at a time, give or take 2. Anything above proves to much cognitive load, and causes discomfort as well as errors. Hence, in a programming context, we need to make sure programmers can use all 7 to improve the code, rather than having to decypher what’s in front of them. In a programming context, we do so by modularizing and standardizing through functions, modules, and packages. Raymond uses the Python random module to hightlight the importance of chunking and modular code. This part was quite long, but still interesting.
For the next two strategies, Raymond quotes the Feinmann method of solving problems: “(1) write down a clear problem specification; (2) think very, very hard; (3) write down a solution”. Using the example of a tree walker, Raymond shows how the strategies of incremental development and solving simpler programs can help you build programs that solve complex problems. This part only lasts a couple of minutes but really underlines the immense value of these strategies.
Next, Raymond touches on the DRY principle: Don’t Repeat Yourself. But in a context I haven’t seen it in yet, object oriented programming [OOP], classes, and inherintance.
Raymond continues to build his arsenal of programming strategies in the next 10 minutes, where he argues that programmers should repeat tasks manually until patterns emerge, before they starting moving code into functions. Even though I might not fully agree with him here, he does have some fun examples of file conversion that speak in his case.
Lastly, Raymond uses the graph below to make the case that OOP is a graph traversal problem. According to Raymond, the Python ecosystem is so rich that there’s often no need to make new classes. You can simply look at the graph below. Look for the island you are currently on, check which island you need to get to, and just use the methods that are available, or write some new ones.
While there were several more strategies that Raymond wanted to discuss, he doesn’t make it to the end of his list of strategies as he spend to much time on the first, chunking bit. Super curious as to the rest? Contact Raymond on Twitter.
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.
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!
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!
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.
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
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!
This overview is curated in the sense that all resources are rated by CourseDuck’s users. These ratings seem quite reliable, at least, I personally enjoyed their top-3 resources sometime the past years:
Recently, I have been watching and greatly enjoying this Youtube playlist of the South-African Sebastian Lague. In a series of nine videos, Sebastian programs a procedural cave generator from scratch. The program generates a pseudo-random cave, following some sensible constraints, everytime its triggered.
The following is Sebastian’s first video in the series labeled: Learn how to create procedurally generated caverns/dungeons for your games using cellular automata and marching squares.
Josh Starmer is assistant professor at the genetics department of the University of North Carolina at Chapel Hill.
But more importantly: Josh is the mastermind behind StatQuest!
StatQuest is a Youtube channel (and website) dedicated to explaining complex statistical concepts — like data distributions, probability, or novel machine learning algorithms — in simple terms.
Once you watch one of Josh’s “Stat-Quests”, you immediately recognize the effort he put into this project. Using great visuals, a just-about-right pace, and relateable examples, Josh makes statistics accessible to everyone. For instance, take this series on logistic regression:
And do you really know what happens under the hood when you run a principal component analysis? After this video you will:
Or are you more interested in learning the fundamental concepts behind machine learning, then Josh has some videos for you, for instance on bias and variance or gradient descent:
With nearly 200 videos and counting, StatQuest is truly an amazing resource for students ‘and teachers on topics related to statistics and data analytics. For some of the concepts, Josh even posted videos running you through the analysis steps and results interpretation in the R language.
StatQuest started out as an attempt to explain statistics to my co-workers – who are all genetics researchers at UNC-Chapel Hill. They did these amazing experiments, but they didn’t always know what to do with the data they generated. That was my job. But I wanted them to understand that what I do isn’t magic – it’s actually quite simple. It only seems hard because it’s all wrapped up in confusing terminology and typically communicated using equations. I found that if I stripped away the terminology and communicated the concepts using pictures, it became easy to understand.
Over time I made more and more StatQuests and now it’s my passion on YouTube.
Many (aspiring) programming professionals competed in this challenge, sharing their learning journeys in domains from web development, machine learning, or data visualization.
With this blog, I wanted to share two of those learning journeys that stood out for me.
First, there’s Avik Jain’s 100 days of Machine Learning code repository on Github. Avik’s repository contains all learning activities he followed during the 53 days of programming he completed. Some of Avik’s entries really stood out, and I particularly liked his educational infographics:
Just look at the wonderful design and visual aids on this decision tree for dummies infographic, pseudocode and all:
Although Avik didn’t seem to have completed the full 100 days, many others did.
I have blogged about Hannah Yan Han‘s 100 days of code project before, but she definately deserves another mention here. Her 100 days revolved around data science, data visualization, and storytelling using both R and Python. You can find her #100DaysOfCode Medium page here, and her associated Github repository here.
For example, one day Hannah explored where instant noodles come from, how they are served, and whether people like them or not.
What I found so great about Hannah’s project is that she picked a novel dataset every couple of days. Moreover, she used a extremely large variety of different visualization formats. All visuals were equally beautiful, but Hannah made sure to pick the right one for the purpose she was trying to serve. If you are interested in data visualization, you seriously should check out Hannah’s 100DaysOfCode Medium page.