Haskell, R, and HaskellR: Combining the best of two worlds (talk at UseR! 2017)

Earlier today, I presented at UseR! 2017 about HaskellR: a great piece of software, developed by Tweag I/O, that allows to seemlessly use R from Haskell.

It was my first UseR!, it was a great experience, and if I had the time I’d like to write a separate blog post about it, as there were things that did not quite align with my prior expectations… Stuff for thought, but not the topic of this post. (Mainly this would be about how the academic talks compared to the non-academic ones.)

So, why HaskellR? If you allow me one personal note… For the ex-psychologist, ex-software-developer, ex-database administrator, now “in over my head” data scientist and machine learning/deep learning person that I am (see this post for that story), there has always been some fixed point of interest (ideal, you could say), and that is the elegance of functional programming. It all started with SICP, which I first read as a (Java) programmer and recently read again (partly) when preparing R 4 hackers, a talk focused to a great part on the functional programming features of R.

For a database administrator, unless you’re very lucky, it’s hard to integrate use of a functional programming language into your work. How about deep learning and/or data science?
For deep learning, there’s Chris Olah’s wonderful blog post linking deep networks to functional programs, but the reality (of widely used frameworks) looks different: TensorFlow, Keras, PyTorch… it’s mostly Python around there, and whatever the niceties of Python (readability, list comprehensions…) writing Python certainly does not feel like writing FP code at all (much less than writing R!).

So in practice, the connections between data science/machine learning/deep learning and functional programming are scarce. If you look for connections, you will quickly stumble upon the Tweag I/O guys’ work: They’ve not just created HaskellR, they’ve also made Haskell run on Spark, thus enabling Haskell applications to use Spark’s MLLib for large-scale machine learning.

What, then, is HaskellR? It’s a way to seemlessly mix R code and Haskell code, with full interoperability in both directions. You can do that in source files, of course, but you can also quickly play around in the interpreter, appropriately called H (no, I was not thinking of its addictive potential here ;-)), and even use Jupyter notebook with HaskellR! In fact, that’s what I did in the demos.

If you’re interested in the technicalities of the implementation, you’ll find that documented in great detail on the HaskellR website (and even more, in their IFL 2014 paper), but otherwise I suggest you take a look at the demos from my talk: First, there’s a notebook showing how to use HaskellR, how to get values from Haskell to R and vice versa, and then, there’s the trading app scenario notebook: Suppose you have a trading app written in Haskell – it’s gotta be lightning fast and as bug-free as possible, right?
But, how about nice visualizations, time series diagnostics, all kinds of sophisticated statistical and machine learning algorithms… Chances are, someone’s implemented that algorithm in R, already! Let’s take ARIMA – one line of code with R.J. Hyndman’s auto.arima package! Visualization? ggplot2, of course! And last not least, an easy way to do deep learning with R’s keras package (interfacing to Python Keras).

Besides the notebooks, you might also want to check out the slides, especially if you’re an R user who hasn’t had much contact with Haskell. Ever wondered why the pipe looks the way it looks, or what the partial and compose functions are doing?

Last not least, a thousand thanks to the guys over at Tweag I/O, who’ve been incredibly helpful in getting the whole setup to run (the best way to get it up and running on Fedora is using nix, which I didn’t have any prior experience with… just at a second level of parentheses, I think I’d like to know more about nix, the package manager and the OS, now too ;-)). This is really the great thing about open source, the cool stuff people build and how helpful they are! So thanks again, guys – I hope to be doing things “at the interface” of ML/DL and FP more often in the future!

Update:
The talk was recorded, and can be viewed here.

Time series prediction – with deep learning

More and more often, and in more and more different areas, deep learning is making its appearance in the world around us.
Many small and medium businesses, however, will probably still think – Deep Learning, that’s for Google, Facebook & co., for the guys with big data and even bigger computing power (barely resisting the temptation to write “yuge power” here).

Partly this may be true. Certainly when it comes to running through immense permutations of hyperparameter settings. The question however is if we can’t obtain good results in more usual dimensions, too – in areas where traditional methods of data science / machine learning prevail. Prevail, as of today, that is.

One such area is time series prediction, with ARIMA & co. top on the leader board. Can deep learning be a serious competitor here? In what cases? Why? Exploring this is like starting out on an unknown road, fascinated by the magical things that may await us 😉
In any case, I’ve started walking down the road (not running!), in a rather take-your-time-and-explore-the-surroundings way. That means there’s much still to come, and it’s really just a beginning.

Here, anyway, is the travel report – the presentation slides, I mean: best viewed on RPubs, as RMarkdown on github, or downloadable as pdf).
Enjoy!

Deep Learning in Action (the less mathy version, this time)

On Tuesday at Hochschule München, Fakultät für Informatik and Mathematik I again gave a guest lecture on Deep Learning (RPubs, github, pdf). This time, it was more about applications than about matrices, more about general understanding than about architecture, and just in general about getting a feel what deep learning is used for and why. (Deep reinforcement learning also made a short appearance in there. Reinforcement learning certainly is another topic to post and/or present about, another time…)

I’ve used a lot of different sources, so I’ve put them all at the end, to make the presentation more readable. (Not only have I used lots of different sources, I’ve also used a few sources a lot. In deep learning, I find myself citing the same sources over and over – be it for the concise explanations, the great visualizations, or the inspiring ideas. Mainly thinking of Chris Olah’s and Andrey Karpathy’s blogs here, of the Deep Learning book, and of several Stanford lecture notes.)

One thing that always gets lost when you publish a presentation are the demos. In this case, I had three demos:

The first two are great sites that allow you to demonstrate the very basics of neural networks directly in the browser: When do you need hidden layers? What role does the form of the dataset play? In what cases can adding a single neuron make a difference between failing at, or successfully solving, a task?
The third demo is just – I think – totally fun: Would you have known that you can play around with your own convolution kernels, just like that, in GIMP? 😉

Deep Learning in Action

On Wednesday at Hochschule München, Fakultät für Informatik and Mathematik I presented about Deep Learning (nbviewer, github, pdf).

Mainly concepts (what’s “deep” in Deep Learning, backpropagation, how to optimize …) and architectures (Multi-Layer Perceptron, Convolutional Neural Network, Recurrent Neural Network), but also demos and code examples (mainly using TensorFlow).

It was/is a lot material to cover in 90 minutes, and conceptual understanding / developing intuition was the main point. Of course, there is great online material to make use of, and you’ll see my preferences in the cited sources ;-).

Next year, having covered the basics, I hope to be developing use cases and practical applications showing applicability of Deep Learning even in non-Google-size (resp: Facebook, Baidu, Apple…) environments.
Stay tuned!

R for SQListas (3): Classifying Digits with TensorFlow

Yesterday at PASS Meetup Munich, I talked about R for SQListas – thanks again for your interest and attention guys, it was a very nice evening!
Actually, in addition to the content from that original presentation, which I’ve also covered in two recent blog posts (R for SQListas(1): Welcome to the tidyverse and R for SQListas(2): Forecasting the future), there was a new, third part this time: an introduction to machine learning with R, by example of the most classical of examples: MNIST, with a special focus on using rstudio’s tensorflow package for R.
While I hope I’ll find the time to write a post on this part too, I’m not too sure when this will be, so I’ve uploaded the slides already and added links to the pdf, github repo and publication on rpubs to the Presentations/Papers section. Enjoy!