Interestingly, the video throughput on Safari has increased lately so we don't have to wait too long before a video gets approved and published. This little guy, for example, I just finished on Thursday and it's already online at the Safari platform. It's by no means an exhaustive survey of the ML field, which is much larger than many people think and it doesn't include A.I. methods only. This video is more of an overview of ML and how it relates to other aspects of Data Science, such as Statistics, A.I., and various applications. So, if you are new to Data Science or want to get a comprehensive overview of the topic to supplement your studies of the subject, feel free to check it out!

Recently an associate of mine and I have started a blog on Medium, focusing on A.I. related topics. There are no articles on it at the moment, but we are actively looking for potential authors of such articles. Every author can have a short bio of him/her and a link to their site of choice (e.g. their company’s site, their own blog, or even an online professional profile of his/hers).

Right now, we don’t have very restrictive requirements regarding the articles, so anything that is related to A.I. (especially its applications and its real-world impact on fields like data science or robotics) qualifies. Also, there is no word restriction so if you want to write a whole mini-book on this blog, you can be our guest!

If you are interested, feel free to let us know either through the comments below, or via a direct email to me (you can use the contact form at the corresponding page of the Foxy Data Science site). Cheers!

So, about 18 months ago I created a video on Safari about how A.I. could benefit Data Science (DS and AI). Even though at that time I was still figuring things out regarding how educational videos work, the vid was immensely popular and even today still attracts lots of views. Considering that all of my recent videos are (much) better than that one, at least technically, this is quite intriguing.

Anyway, fast forward to September last year. As I was walking in the streets of suburban Seattle, thinking about what to do next (my Data Science Mindset, Methodologies, and Misconceptions book had just been released), I decided to write another book, one about A.I. since this topic continued to fascinate me, while it was becoming a popular topic among various data scientists. So, I pitched the idea of a new book to Steve Hoberman and after sorting out the details, we got a contract going. However, due to various reasons we decided to start the book in January.

The whole project was quite a turbulent one, with my co-author dropping out around March, leaving me in a very difficult situation. Yet, I decided that the book was worth completing. Fortunately, another data scientist / A.I. expert decided to join me in this endeavor, Yunus E. Bulut, who I got acquainted with through Thinkful. Long story short, after a few discussions about the project he had a contract of his own as a co-author.

Three months later, the first draft was complete. Of course the book went through a lot of revisions since then, partly because the technology was changing and partly because there were a lot of topics in this book, which was difficult to coordinate and merge into a coherent whole. Also, at one point Julia reached adulthood as a programming language (v. 1.0) so we had to update the code for the chapters that had programs in Julia.

So, after a feverish summer, plagued by heat waves and other obstacles, we finished the edits (at least the most important ones, since a book is never really finished!) and the book went to the press. Now, it is finally available for you to buy at whatever vendor you prefer. Check out the publisher's site for more details. Cheers!

So, recently I decided to make a couple of videos on niche topics, namely the Business Aspect of A.I. in Data Science and  Extreme Learning Machines (ELMs). These vids are now available on Safari (here and here). Enjoy!

Note that in order to view these vids in their entirety you'll need a subscription to the platform. The latter enables you to view other materials, including a large variety of technical books as well as all my other videos. Cheers!

Last week I’ve finished my part of the final corrections stage of the new technical book I’d been working on for the past few months. My co-author, Yunus, has done the same, so the book should be in the press later this month! Hopefully, you should be able to purchase it soon, either from the publisher’s site, or from some other vendor (e.g. Amazon). Just wanted to share that with you all. Once the book is out there, I’ll be sure to make an announcement about it here on this blog. Cheers!

As a famous Chinese sage once said, "a car is more than the sum of its parts." It's intriguing how this applies not just to ancient vehicles in the Orient, but also to a special kind of data science models called ensembles. So, if you want to learn more about this fascinating topic and how it is useful in a data science setting, check out my latest video on the Safari platform.

Note that you will need a subscription to the Safari system in order to view this vid in its entirety. However, with such a subscription you'd be able to access a lot of other material on a variety of technical topics, including all my other videos. Cheers!

Introduction
The previous week has been intense as I was working on a part of the proposal for a new project, attending a conference, and figuring out some things about my publication-related endeavors. With all that in mind, it was natural that I didn’t post anything on the blog, even though I wanted to. However, as my focus is always on quality, I didn’t want to just publish a rushed post or a simple announcement. That’s why I waited until now to get a new post out.

The Event of the Decade
On 8/8/18 the new release of Julia came out. This wasn’t just any release though, but the big one: 1.0. It is really hard to overestimate the importance of this release, even if the most conservative Julia users still feel that it would take a few months before the full force of v. 1.0 will reach the world. After all, just because Julia is now production ready, it doesn’t mean that everyone using it can benefit from this the same way, since the packages people depend on may take some time before they are fully compatible with the new release. Nevertheless, those who prefer to rely on our own code primarily can experience the benefits of Julia right now. Whatever the case, the fact is that Julia has now entered a new era, since it has proven itself to be robust and even faster than ever before.

To give you an example of that, in the conference there was a talk about how Julia is applied in Robotics, via a specialized package some Robotics researcher developed recently. Even though this guy had worked with C++ before for the same project, he eventually shifted to Julia for the vast majority of the code, since it was good enough (i.e. sufficiently fast and reliable) to perform challenging optimization-related tasks in real-time. To be exact, the operations were 36% faster than real-time, enabling a robot operation frequency of 1000 Hz, at least in the simulations he was conducting. At the time of this writing, no other language has accomplished that, without having significant dependencies on C libraries.

Ramification of Version 1.0 in Data Science and A.I.
But how does all this affect us, as data science and A.I. professionals? Well, Julia isn’t evolving merely on the Base package or the fairly niche application of Robotics. In fact, there are now full-fledged packages that cover a variety of data science related applications, including deep learning models. In the conference there was a talk about the Knet package, for example, which is a deep learning package built entirely on Julia. Personally I don’t know any other deep learning tool that has been built entirely on a data science language (I don’t consider C++ to be such a language by the way, since data scientists tend to use high-level languages mainly). What’s more, this deep learning tool has comparative performance with other more established frameworks, while in one of the benchmarks it outperformed all of them.

But data science is not just deep learning. There is a significant part of it that has to do with more conventional methods, mainly deriving from Statistics. What about Julia’s role in all that? Well, Julia has a number of fairly mature packages in Stats, including Bayesian Stats. What’s more, there is a new book being written right now on Stats with Julia, by a couple of academics who teach Stats in a university in Australia. So, it’s safe to say that Julia is pretty evolved in this aspect of data science too.

More specialized parts of data science, such as Graph Analytics also have corresponding packages in Julia, while the LightGraphs package I talked about in my Julia for Data Science book, is still out there, now better than ever. Data engineering packages also exist, while there are several packages on optimization too, something data science can benefit from greatly, for the more challenging problems tackled.

Now What?
From all this, I believe it’s fair to say that the age-old argument that “Julia is not ready for DS / A.I. because x, y, z” is now as ridiculous as the belief that the number of available libraries is what makes a language more suitable for data science. Sure, packages can help, but it’s mostly due to their quality, not their quantity, while how fast a language runs is an important factor when analyzing the truckload of factors in a modern data model. That’s not to say that Python, Scala, and other data science languages are not useful any more, but ignoring the value of Julia in the data science / A.I. arena is silly and to some extent unprofessional.

This past week we received the first round of feedback from our publisher, so my co-author and I have been feverishly working on refining the book, making clarifications where necessary and adding some content for better context here and there (mostly there). So, after a week’s worth of editing we have completed the revised version of the book which we’ve sent to the publisher this weekend...

Also, this past week I wrote three articles for one of the blogs of the company I work with in London, so it’s been quite busy writing-wise. These are all part of the SEO plan for one of the websites of the company, so they are a bit dry but they are still interesting to read.

What’s more, on my free time I’ve been thinking about A.I. Safety and creating mind maps on the topic. In fact, until further notice, that’s going to be my main past-time from now on, that and creative writing. After all, that sci-fi novella of mine isn’t going to write itself!

So, with all that going on, I didn’t have the chance to put together an article for this blog this week. Stay tuned though since the ones I have in mind are going to be unique and intriguing...

This is a topic that I'm pretty confident hasn't been featured much anywhere in the pop data science literature. Although it is quite well-known in the research sphere, most non-PhDs (and some PhDs too!) may have never heard about it, or why it is useful in day-to-day data science work. So, if you are one of those people who are curious and interested in learning even the less popular topics of our field, feel free to check it out on Safari.

Note that although I made an effort to cover this subject from various angles, this is still an introduction video to its topics. Also, some experience in data science would be immensely useful, otherwise the video may appear a bit abstract. Whatever the case, I hope you find it useful and use it as a jumping board to new aspects of data science that you were not aware of. Cheers!

First of all, I'd like to thank all of you for visiting this blog and checking out the various posts I've put up over the past couple of years. I appreciate it, even if I don't express it!

Lately it has come to my attention that many people comment in various posts for the sake of commenting. You never get to see these comments because I delete them or mark them as Spam. The reason is simple. Even if they don't directly promote this or the other company or brand, they are:

1. too generic to add value to anything (sounding very much like something a certain politician would say when he was oblivious about a topic he's asked)
   
2.  void of information (honestly, some of them look like they have been written by an A.I. created for this purpose and as I know a thing or two about NLP and chatbots, I can spot this sort of comments from miles away)
   
3. sometimes entirely irrelevant to the post they are referring to.
   

Naturally, even if a comment doesn't directly promote this or the other brand, it is accompanied by a link, so there is SEO value in it. Having served as an SEO manager in a company once, I'm quite familiar with these tricks. So, it seems that the intent of these comments is not aligned with the intent of this blog, which is to inform people about certain data science and A.I. related topics and challenge conventional ideas and preconceptions about them. I am considering removing the commenting option from now on, so if this happens, know that it is in order to avoid these noisy comments.  Whatever the case, you are always welcome to contact me directly, like some of you have done already.

Again, thank you for reading this blog. I look forward to sharing more fox-like insights in the future!