Nutrient Dense High Protein Lunch – [New Experiments]

Let’s start with some context…

For now, I’ve stopped intermittent fasting. I tried many variations of this type of meal timing and all of them, with adequate customization, seem to work. The one that appears to be most convenient for me is 18-6 with 2 meals; the last one I experimented with was one meal a day.

New York City Personal Video Compilation – [Landmarks]

My Steemit Crypto Experience – Personal Reflections [One Year Later]

It was about a year ago (circa July 26, 2016) when I decided to join steemit.com, a social platform that rewards its users for posting, commenting, voting, and curating content.

You can think of it like a facebook that rewards you with crypto-currency for your participation.

Atop of that, there is no central authority behind the ‘wheel’, like with conventional social media. Of course, there are games of power; but most of what happens on this platform is relatively transparent and all ‘transactions’ (transfers, withdrawals, etc.) and ‘operations’ (comments, posts, votes) can be viewed by anyone, by using different tools or by accessing the Steem blockchain programmatically (which I’ve been doing a lot).

This type of decentralized social media is likely to catch, in my view. We are averse to being controlled, but most of us are unaware of our actual ‘puppet’ status. I don’t believe in conspiracies, but I know that for-profit companies need to successfully meet their financial agendas. To get a very small sense of you being exploit, I’d recommend listening to this discussion between Sam Harris and Tristan Harris.

Phising for Phools – The Economics of Manipulation and Deception [Animated]

In this animation I share a few thoughts and take-home messages from reading Akerlof and Shiller’s book, Phising for Phools:

Mid-Summer Physique Update – [July 2017]

This is a another very quick physique update video I posted yesterday on my Youtube channel:

ACTN3 Gene and Sports Performance – A Look into 1,750 Genomes [OpenSNP]

While phenotypes are most often defined by a combination of genetic mutations (SNPs and other), there are single gene modifications that seem to have powerful phenotypic effects – think of diseases driven by single nucleotide polymorphisms. In such circumstances, you can’t do much on the ‘nurture’ side of things – when the ‘nature’ or the genetics side of it is so determining.

We’re going to take a brief look at a phenotype that seems to be strongly affected by mutations in the alpha-actin-3 or ACTN3 gene. Specifically, we take advantage of the ‘opennes’ of the OpenSNP platform where users share genetic and phenotype data.

Even more specifically, we’re looking at rs1815739 (SNP) which refers to the coding of a premature stop codon in ACTN3, which is a muscle protein located on chromosome 11. This genetic mutation seems to affect muscle performance.

Beginning Machine Learning – A few Resources [Subjective]

I’ve been meaning to write this post for a while now, because many people following the scikit-learn video tutorials and the ML group are asking for direction, as in resources for those who are just starting out.

So, I decided to put up a short and subjective list with some of the resources I’d recommend for this purpose. I’ve used some of these resources when I started out with ML. Practically, there are unlimited free resources online. You just have to search, pick something, and start putting in the work, which is probably one of the most important aspects of learning and developing any skill.

Since most of these resources involve knowledge of programming (especially Python), I am assuming you have decent skills. If you don’t, I’d suggest learning to program first. I’ll write a post about that in the future, but until then, you could start, hands-on, with the free Sololearn platform.

The following resources include, but are not limited to books, courses, lectures, posts, and Jupyter notebooks, just to name a few.

I Walked 7.55 Kilometers per Day for a Month – [Insights]

May 2017 is ranked number #2 for the monthly average steps I walked since the default pedometer has been running continuously on my phone.

So, in May 2017 I completed an average of 7.55 kilometers or 9,667 steps per day, which burned, according to the pedometer, an additional 347 kcals, on top of the rest of my ‘energy out’ for each day. However, estimates like this are notoriously inaccurate.

Number #1 on the list is September 2014, when I completed an average of 10,256 steps.

What I’ve Been Reading Recently – My Bookshelf #11

The last time I wrote this type of post was at the beginning of 2017 when I was telling you about the 105 books I read in 2016.

It’s less likely I’ll achieve that type of performance in 2017, and that’s fine. I still prioritize on reading and listening to books everyday. So, that’s my only important metric for now.

Similar to what I said in my last post, the majority of the books that go through my hands are non-fiction: science books, programming books, textbooks, biographies, and the like).

I enjoy reading on my tablet because it has a stylus-pen and the app I read the books on has a lot of features that allow for annotations, highlights, exporting, synching, and other conveniences that make the reading experience a breeze. A detailed explanation of this can be found here.

I find a lot of pleasure in reading physical books too. As a matter of fact, I managed to read 11 books during a recent stay in New York, which lasted for a couple of weeks; and that happened within a very busy and hectic daily schedule. This goes to show the importance of prioritization for goal accomplishment. I have to mention that I completely stopped reading on my tablet while in NYC.

Machine Learning with Scikit-Learn – The Cancer Dataset [Work in Progress]

If you’ve been following my Youtube channel, you know I’ve been doing machine learning tutorials on the cancer dataset that comes preloaded with scikit-learn in Python.

The tutorials follow a path similar to what’s in Andreas Muller and Sarah Guido’s book on machine learning. This allows me not only to pass on knowledge to others who are interested in the details, but also to strengthen my own knowledge of these concepts.

24 video tutorials in and I realize that this is going to be a long series. I want to take the appropriate time to look into the details and to apply the very specifics for each algorithm and concept as we explore the cancer dataset.

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