I am an informatics master student at the University of Lugano, Switzerland where in the coming months I will focus on work of deep learning applied to text generation. Before that I build my own GPU cluster and developed algorithms to speed up deep learning on GPU clusters.

I occasionally take part in Kaggle competitions where I have reached world rank 63.

In the past I studied applied mathematics at the Open University, and did a dual apprenticeship as Mathematical and Technical Software Developer where I worked on the automatization of manufacturing plants. There I mainly developed systems of automatic data analysis and automatic reporting and the creation of information interfaces between manufacturing equipment and enterprise resource planning (ERP) systems like SAP.

Besides deep learning, I am also very interested in understanding the human brain, human nature, the human condition and their evolution. In my spare time I like to study and think about fields aligned to these topics.

Feel free to contact me at firstname.lastname@gmail.com; if you have questions regarding deep learning, I prefer that you post your questions as comments on one of my blog posts (or on this page if it does not fit to any blog post); this way all people can profit from your questions and my answers.

19 thoughts on “About

  1. Are you building your own ml machines or buying gaming machines with Nvidia 980 gpu’s? What is a good desktop to buy for ml? I do not really want to put together my own computer.


    • It is always significantly cheaper to build one’s own machine and you can customize the parts better. Gaming machine often have some parts that are better than needed and some important parts which worse than needed. However, for single GPU systems overall performance is determined by the GPU (about 90%) and not by the surrounding hardware (about 10%); so any gaming rig with a GTX 980 or any other good deep learning card will be all that you need – all choices are good.

      Liked by 1 person

    • The Cousera ML course by Andrew Ng is excellent and will get you acquainted with all different basic machine learning algorithms. With an economics background you may already know some of these algorithms, but a refresher might be nice.

      Once you done that the main goal would be to become good at feature engineering. This is a very difficult task, and there is little information available – you will mainly learn through experience. The most important resource will be Kaggle forum threads of past competitions where the winners explain their methods; try to go through all the old competitions and understand the methodology of the winners – you will learn a lot from this! Also look at “Beating the benchmark threads” for each competition. There you will usually be able to learn easy basics to get started on problem.

      After this you will mainly learn through experience: I would use python and the scikit-learn (sklearn) module and start to do as many competitions as you can. Try to apply what the winners mentioned in their forum posts. From here it is practice, practice, practice: Initially you will have little success, but do not be discouraged by this, this is normal; it will take a few months until you will score well, and about 1-2 years until you will perform at masters level; 3-7 years and you will perform at world class level.

      Hope this helps – good luck!


  2. Hi. Thank you very much for awesome posts about hardwares. It helped me a lot. By the way, I am just curious – what library do you use for your deep learning networks?


    • Thanks for your feedback. Currently, I am developing my own deep learning library from scratch, which will feature automatic GPU computation for any algorithm and numpy interface. Besides that I found Torch7 to be best (among theano/pylearn2 and caffe): It is easy to use, features many highly optimized routines that other libraries do not offer and it is easy to extend.


  3. Hey! Just saw your blog. Nice endeavor. I’m an undergrad myself as well. I saw your article on Hardware for Deep Learning. I had been thinking of building a machine for my research. Can you shell out some details about your system?


      • Your website is amazing. Thank you so much for putting these together.

        What is the motherboard you are using?

        I am putting together the following – what do you feel? If I use AMD CPU with PCIe 2.0 then the cost could come down by 400 $. Would I be sacrificing lot by using a PCIe 2.0 ?

        Thank you,

        Component Selection Price

        Intel Core i7-4820K 3.7GHz Quad-Core Processor
        $319.99 Buy
        CPU Cooler

        RAIJINTEK AIDOS BLACK 48.6 CFM Sleeve Bearing CPU Cooler
        $24.21 Buy

        Asus Rampage IV Gene Micro ATX LGA2011 Motherboard
        From parametric selection (show)
        $229.99 Buy

        G.Skill Ripjaws X Series 8GB (2 x 4GB) DDR3-1600 Memory
        $59.98 Buy

        Western Digital Caviar Blue 1TB 3.5″ 7200RPM Internal Hard Drive
        $51.49 Buy
        Video Card

        EVGA GeForce GTX 960 4GB SuperSC ACX 2.0+ Video Card
        $238.98 Buy

        Corsair 100R ATX Mid Tower Case
        $47.99 Buy
        Power Supply

        Corsair CX 500W 80+ Bronze Certified Semi-Modular ATX Power Supply
        $66.98 Buy
        Total: $1039.61


      • I have an Asus Rampage and a Gigabyte motherboard; for most people the Asus Rampage did work fine, but for me it would not work with 4 GPUs. So I switched to my Gigabyte motherboard which just works fine with 4 GPUs. Generally there is not much wrong you can do with a motherboard — in my case it was just bad luck with the Asus Rampage.

        I have no data from AMD CPUs, but because CPUs are not that important, I do not think you can go wrong with that. If you roll with PCIe 2.0, then you really should only work with one GPU (0-10 % performance decrease); otherwise, if you plan to have multiple GPUs in the future try to get a PCIe 3.0 CPU and motherboard (4 GPUs about 40% performance decrease for PCIe 2.0). AMD vs. Intel should be at most 2-5 % performance decrease.


  4. An undergrad?
    Really surprised me, your articles are well organized that I think you might be an young professor or engineer!
    I am new to deep learning, and I find many preliminary concept from your blogs. That helps me a lot, since my mentor just sends me a series of papers, I am confused by the jargon.


    • Thank you! It is quite ironical, because I flunked out of high school due to my writing. I am a dyslexic and while I have a terrible time to write with pen-and-paper I am much better at this when I can use a computer. This might seem quite paradoxical, but this is actually quite common for dyslexics: Dyslexics can often write well, but they will need help with the writing to just do that — I am so grateful that I live in a time where computers exist that can help me with that.


      • I have to agree with jokeren! Very impressive blog and great subject area expertise from an undergrad. Look forward to following your blog as I begin to learn about this field! Do you have a space where I can ask you or other experts questions?


      • Thank you! It is best to ask questions in the form of a comment under the most appropriate blog post or here for anything else, that way the answers and questions will be visible to anyone else.


    • Unfortunately, I will move to Switzerland soon where I will study at the University of Lugano. Currently, I am quite busy with organizing my move. So if you want to discuss/talk about things the best way to reach me is via email. If you have some questions you are always free to ask them here, so that other people will be able to see the Q&A here.


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