If you’ve read through how Support Vector Machines work, you probably know the linear simple SVM might not work in all cases… but how does it fail? Let’s take a look at an example I tried like to my simple example… but change it to be a larger space than just 4, and separated with a region in the middle, and the region around it (positive, negative labelled areas to learn):
Continue reading “Machine learning SVM – the usefulness of kernels”Human pose estimation with Python and Gluoncv
Human pose estimation is something useful for robotics/programming as you can see what position a person is in a picture. For last weekend’s Hackrithmitic I did an experiment for fun using computer vision pose estimation. To start with I found several possibilities with available libraries:
- Tensorflow js has been used to say, don’t touch your face, but it takes a massive amount of cpu.
- Openpose is a popular one, only licensed for noncommercial research use, and there is a Opencv example for it that doesn’t quite show how to use it.
- AlphaPose is supposedly faster and has a more clear license and possibility for commercial use – if you want that as a possibility. I checked out the install instructions and worked but for “python3” instead of “python”. It also misses obvious step of installing cuda for your Nvidia system before running.
- GluonCV is another, which seems more user friendly. This one I was able to get running in a few minutes with their example:
Slide rule enters the 21st century
Some time ago I came across this online tool in a newsletter article – this is a very cool slide-rule-emulator that will not just let you move two slides, but actually slide it for you as you run an equivalent digital calculator calculation to the right!
If you haven’t ever used a slide rule before, it works on properties of logarithms, and the principle that log(a)+log(b) = log(a*b). Now it wouldn’t be very interesting to just have two normal rulers together, as sliding and adding would just let you do problems like 5+5 = 10 or 50+50 = 100 if you scale the numbers. With logarithmic scale, the spacings are off and it allows you to do multiplication in adding the numbers.
Continue reading “Slide rule enters the 21st century”Microsoft Math Solver review
Years ago if you wanted a program to explain steps in mathematics, algebra or other complex math as a tutor would, you would have to buy a specialized software package built for some specific operating system (I forget the name… it may still be around?) Of course there was always open source software like Maxima to do powerful symbolic (or numeric, or graphing) math, but to know what to do one almost needs a manual, and while extremely powerful it was not helpful for beginners. I recently found a similarly useful free math solver on Microsoft’s site, https://mathsolver.microsoft.com:
Continue reading “Microsoft Math Solver review”Possible ABC Proof Conjecture brings Primes into Prime time news again!
Recently a possible proof of the ABC Conjecture has been in the news. Although the proof of this is hundreds of pages long and not really a fun read for most people, this reminded me of the prime spiral, “Ulam spiral” which we explored years ago at a meetup.
The interesting thing about ABC Conjecture is that no matter what examples or counterexamples you find to the inequality, it does not prove or disprove the theory as to where there are only finitely many specific triples to solve the inequality.
Ulam’s spiral is also a look into prime numbers, but from a visual perspective. Nothing to “prove” here but to see an interesting pattern within numbers. It was supposedly thought of by Stanislaw Ulam during a meeting, doodling numbers, and it was later popularized by Martin Gardner’s writings. It is a great way to have some fun learning how to use Matplotlib to draw up some interesting charts, too:
Continue reading “Possible ABC Proof Conjecture brings Primes into Prime time news again!”Mathematics with Pi – and earth measurement with network requests
In Ian Stewart’s book, Professor Stewart’s Casbook of Mathematical Mysteries, he writes about an easy way one might prove that the earth is not flat. His “easy” proof can be done by booking some flights and timing them… or, simply looking up actual flights from certain cities to other cities. If it is much much shorter for a certain flight from A to D while A to B to C to D in a nearly straight line is much longer, it’s effectively a proof you can go around the world without falling off…
Continue reading “Mathematics with Pi – and earth measurement with network requests”Happy Palindrome Day! Again and again!
As you may have heard, 02-02-2020 is a very “palindromic” day today. Especially so in that in either date formatting you use, the numbers are the same backward as forward (assuming you use 02 not “2” that is). Matt Parker was quite excited, in fact he was beside himself in his latest math video:
Continue reading “Happy Palindrome Day! Again and again!”Euclid’s Doodle – and writing a visualization with Matplotlib
On page 110 of Professor Stewart’s Casebook of Mathematical Mysteries, he shows a neat visual way of calculating the GCD (Greatest common denominator, aka HCF, Highest common factor. Given a box with sides of two different lengths, draw squares from the lesser side until you can draw no more. Then continue from the corner the other direction. The smallest square has edge length of the GCD!
Continue reading “Euclid’s Doodle – and writing a visualization with Matplotlib”Analyzing Scott Flansburg’s Nines Trick
There’s an old math trick that goes like so – choose any number… say 171…. add the digits and subtract.
171-9 = 162
Take its sum of digits and subtract them…
162 – 9 = 153
Take its sum of digits and subtract them… Continue reading “Analyzing Scott Flansburg’s Nines Trick”
Neural Networks Part 2: Learning Pi
Thousands of years ago, probably around the invention of the wheel, and before the time of Solomon, humans must have been measuring various objects… calculating some distances, and wondering, is there a better way to measure how far around the outside of a wheel is compared to its diameter? Continue reading “Neural Networks Part 2: Learning Pi”