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]]>I just wanted to do a quick follow up to my recent blog post, which discussed the performance metrics I think might be appropriate for use in medical AI studies. One thing I didn’t cover was the reason we might want to use multiple metrics, or the philosophy behind choosing the ones I did. So today, […]

via The philosophical argument for using ROC curves — Luke Oakden-Rayner

]]>I have a little secret: I don’t like the terminology, notation, and style of writing in statistics. I find it unnecessarily complicated. This shows up when trying to read about Markov Chain Monte Carlo methods. Take, for example, the abstract to the Markov Chain Monte Carlo article in the Encyclopedia…]]>

Such a great clear explanation of a topic I’ve often wondered about.

I have a little secret: I don’t like the terminology, notation, and style of writing in statistics. I find it unnecessarily complicated. This shows up when trying to read about Markov Chain Monte Carlo methods. Take, for example, the abstract to the Markov Chain Monte Carlo article in the Encyclopedia of Biostatistics.

Markov chain Monte Carlo (MCMC) is a technique for estimating by simulation the expectation of a statistic in a complex model. Successive random selections form a Markov chain, the stationary distribution of which is the target distribution. It is particularly useful for the evaluation of posterior distributions in complex Bayesian models. In the Metropolis–Hastings algorithm, items are selected from an arbitrary “proposal” distribution and are retained or not according to an acceptance rule. The Gibbs sampler is a special case in which the proposal distributions are conditional distributions of single components of a vector parameter. Various special cases…

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Data science has been a hot term in the past few years. Despite this fact (or perhaps because of it), it still seems like there isn’t a single unifying definition of data science. This post discusses my favourite definition. Data Scientist (n.): Person who is better at statistics than any…]]>

Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.

— Josh Wills (@josh_wills) May 3, 2012

One of my reasons for doing a PhD was wanting to do something more interesting than “vanilla” software engineering. When I was in the final stages of my PhD, I started going to meetups to see what’s changed in the world outside academia. Back then, I defined myself as a “software engineer with a research background”, which didn’t mean much to most people. My first post-PhD job ended up being a data scientist at a small startup. As soon as I changed my…

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This is the first post that makes justice to the blog’s motto: show me the code motherfucker. In this and the next n posts with the title “Neural networks in a nutshell – k” I will talk about artificial neural networks, showing concepts (theory) and code…]]>

This is the first post that makes justice to the blog’s motto: **show me the code motherfucker**. In this and the next *n* posts with the title “Neural networks in a nutshell – *k*” I will talk about artificial neural networks, showing concepts (theory) and code (practice). The codes will be written in Python without any fancy library as NumPy, SciPy or PyBrain just because:

- I don’t know how to use any of these.
- I don’t have time to learn them now.
- The focus is in the concepts, not in the performance.

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Kronecker famously wrote, “God created the natural numbers; all else is the work of man”. The truth of this statement (literal or otherwise) is debatable; but one can certainly view the other standard number systems as (iterated) completions of the natural numbers in various senses. For instance: The integers…]]>

Kronecker famously wrote, “God created the natural numbers; all else is the work of man”. The truth of this statement (literal or otherwise) is debatable; but one can certainly view the other standard number systems $latex {{bf Z}, {bf Q}, {bf R}, {bf C}}&fg=000000$ as (iterated) completions of the natural numbers $latex {{bf N}}&fg=000000$ in various senses. For instance:

- The integers $latex {{bf Z}}&fg=000000$ are the additive completion of the natural numbers $latex {{bf N}}&fg=000000$ (the minimal additive group that contains a copy of $latex {{bf N}}&fg=000000$).
- The rationals $latex {{bf Q}}&fg=000000$ are the multiplicative completion of the integers $latex {{bf Z}}&fg=000000$ (the minimal field that contains a copy of $latex {{bf Z}}&fg=000000$).
- The reals $latex {{bf R}}&fg=000000$ are the metric completion of the rationals $latex {{bf Q}}&fg=000000$ (the minimal complete metric space that contains a copy of $latex {{bf Q}}&fg=000000$).
- The complex numbers $latex {{bf C}}&fg=000000$ are the algebraic…

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It is widely forecasted that a shortage of skills in data science and analytics will mean a great deal of money is wasted through missed opportunities in coming years. Traditional academic establishments have begun to move to fill the gap. However, most courses teaching the hot topic skillsets such as […]

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]]>For those who aren’t regular readers: as a followup to this post, there are four posts detailing the basic four methods of proof, with intentions to detail some more advanced proof techniques in the future. You can find them on this blog’s primers page. Do you really want to get…]]>

*For those who aren’t regular readers: as a followup to this post, there are four posts detailing the basic four methods of proof, with intentions to detail some more advanced proof techniques in the future. You can find them on this blog’s primers page.*

Remember when you first learned how to program? I do. I spent two years experimenting with Java programs on my own in high school. Those two years collectively contain the worst and most embarrassing code I have ever written. My programs absolutely reeked of programming no-nos. Hundred-line functions and even thousand-line classes, magic numbers, unreachable blocks of code, ridiculous code comments, a complete disregard for sensible object orientation, negligence of nearly all logic, and type-coercion that would make your skin crawl. I committed every naive mistake in the book, and for all my obvious…

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Zmob, my first (and only) original game. By the end, the breadth and depth of our collective knowledge was far beyond what anyone could expect from any high school course in any subject. Education Versus Exploration I’m a lab TA for an introductory Python programming course this semester, and it’s been…depressing. I…]]>

Zmob, my first (and only) original game.

*By the end, the breadth and depth of our collective knowledge was far beyond what anyone could expect from any high school course in any subject. *

I’m a lab TA for an introductory Python programming course this semester, and it’s been…depressing. I remember my early days of programming, when the possibilities seemed endless and adding new features to my programs was exciting and gratifying, and I brimmed with pride at every detail, and I boasted to my friends of the amazing things I did, and I felt powerful. The world was literally at my fingertips. I could give substance to any idea I cared to entertain and any facet of life I wanted to explore. I had developed an insatiable thirst for programming that has lasted to this very day.

My younger self, if programming were more noodley.

The ironic thing is…

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