Currently, my routine is set up to produce 52 blocks of content per year. Each week one block of content is getting generated. Within that cycle I have time devoted to writing on the weekend. Both Saturday and Sunday morning I wake up early and focus for several hours without interruption. We are in the third year of this 52 block creation format. The previous two years were moved into a manuscript format and packaged as books. Each of the blocks is shared out as a Substack post along the way. All of that is geared toward my efforts to learn, understand, and explain complex topics. That is the routine that I have setup and am implementing as part of both my daily writing plan and the research trajectory I have set up for myself. All that rolls up into my five year writing plan and I have been successful in adhering to the plan.
This pattern of production works for me and I’m ok with sustaining it. One of the things I need to really focus on doing is converting some of the blocks into research notes, literature reviews, and the seeds of academic papers. Last year I built a solid literature review in Overleaf and was able to share it out. Pretty much every part of that effort was rewarding. It was good research and the effort put into that made sense. This year we have moved from post 105 to where I am currently working on post 124. That pretty much means that 19 blocks of the 52 for this year have been expended. All of that effort did not yield another publication shifted over to Overleaf for extended sharing. At the moment, I’m deeply considering what that means to have spent the time and effort on that writing effort, but not have turned the corner from building blocks of content to creating publications.
All that being said, I’m trying to figure out how to take my remaining backlog for the rest of the year and either mix and match blocks to build something or change out some of the remaining blocks for the year to help support the mission of creating better literature reviews. I know that the best possible plan is to probably just sit down and write down the top 5 literature reviews toward the bleeding edge of technology I would like to read and then just produce the ones that do not exist. Working on things within that process is probably the right way to move things forward. Getting to a posture where my routine is generating the output I want over time is really the outcome I’m looking to achieve. Having a routine is great and it is the first step in the process. A good next step is understanding the outcomes of that routine. That is what I have been trying to think about within the last 500 words or so of prose.
Each of those 52 blocks right now is created in a Google Doc and that is where the content stays within the 5 week planning and review cycle. For the whole year I work on content within that document and pull out completed works to share them in Substack. I’m trying to figure out if I should be publishing the content on the blog as well. No real conflict of obligation exists in doing that type of doubling up on posting the content. Generally, each blog post is created in a separate stand alone Google Doc and then that word processing document is just left in storage afterwards. That is very different from the 52 blocks of content where towards the end of the year I take the time to format the content back into a Microsoft Word document and prepare that manuscript for both editing and publication. From what I can tell, old Substack posts don’t really get a ton of traffic and at some point I’m sure that platform will cease to exist. My blog will exist until approximately 5 years after my efforts cease. I tend to pay in advance for domains and hosting.
During the course of the 4th of July I got a chance to read a few PDFs of papers. Being able to write within the academic tone that papers usually have is a skill. Sometimes the papers include great scientifically based research, but are difficult to follow due to being poorly written. In the machine learning space this is one of those things that happens and could be compounded due to the mathematics the author is trying to share. Within the abstract and introduction things will start out on the right academic footing, but then as the mathematics start to get introduced things will veer off into the wild unknown. Most of the mathematics that gets shared within the machine learning space is not a provable theorem or something that you can easily break down and check. Every single time somebody starts to walk me through a series of equations in a paper I start to evaluate the work. Most of the time you cannot check the actual output given that the equations in the paper are implemented as code. Generally, that code is not shared so you cannot work your way backward from the code to the paper or the other way around to figure out what the author did for the actual delivery of the mathematics.
The other piece of the puzzle that often worries me is that the equation presented in the paper is theoretical as an implementation and they are using an algorithm built into software that was already written. Within this example the author did not implement the mathematics within the code and probably is not deriving a perfect reflection of equation and implementation in the paper. Being able to run code as a part of a software package and being able to work through the equation using LaTeX or some other package to articulate it within an editor are very different things. I would have to work it out with pen and paper and then bring it over to the paper after the fact. Generally, I would be working with a known transform or algorithm within the machine learning space. It would be unlikely that I would be advancing the equations denoting the mathematical machinations beyond the original efforts. Within the coded implementation I might do something in an applied way that could ultimately be represented as a novel piece of mathematics. However, most researchers would be better off presenting the code vs. trying to transform the code back into a series of mathematical representations within an academic paper.
It might very well be a good topic for a research paper to do an analysis of the equations presented in mathematical form in the top 100 machine learning papers by number of citations. It might be interesting to see the overlap in the equations across the paper. Alternatively it might be very illuminating to see if no overlap exists given that it’s possible outside of a shared statistical foundation the researches are not working from a shared mathematical base. A book on the mathematics of machine learning would be interesting to read for sure assuming it built from the basics to the more complex corners of the academy of knowledge surrounding the topic.
My writing schedule has been updated for Sunday on a go forward basis. The schedule now includes a morning writing session to review my Substack post and work on academic articles for 1-3 hours, publishing a note on LinkedIn about the last Substack post, and sending a Tweet about my last Substack post. Working within that writing schedule will probably help increase the readership of the newsletter. To pick up my subscribers the content is going to need to be posted to Twitter and to LinkedIn. This week I even elected to login to my highly dormant Facebook account to share the link.
Over the last two months my writing productivity has really plummeted. That is something that needs to be rectified here one day at a time. Reversing a trend like that is just about spending the time at the keyboard to write larger blocks of prose. I have a manuscript in progress that is entitled, “On Machine Learning: The Lindahl Letters of 2021.” I’m going to publish my entire set of 2021 Substack posts in one long manuscript format. All of the content has been put into a basic manuscript draft and I’m currently working on starting at the beginning and reworking the content from start to finish before publication. This is one of those things that I’m probably going to need to send to my editor before final publication. Right now the manuscript is about 165 pages long, but that could easily get longer before publication.
Earlier this morning I just could not manage to get back into the flow of writing. A trip to the donut store and a stop to get some coffee happened, but a bunch of time writing never occurred. Getting back on schedule with a writing routine has to be my primary focus moving forward. Yesterday I created a draft post with two solid paragraphs of content. For some reason that was where my productivity stopped. I’m about to watch the Kansas City Chiefs game on Sunday night football. During that game my plan is to try to rework the content and get it ready for publication on Friday.
This week I sat down and completed the draft of the week 15 edition of The Lindahl Letter. That one is titled, “What are people really doing with machine learning?” The draft is now complete for publication on Friday May 7, 2021 at 17:00 hours Denver time. You may have guessed that today it seemed like a good idea for me to finish up that draft instead of starting my day with this ongoing writing project to produce a page of prose each and every morning. That is how my day started and it turned out well enough. I’m pretty sure the upcoming missives are going to be a little shorter for a bit until I have time to catch up and work ahead of my weekly delivery schedule. Right now I have a list of topics that pushes things out to week 37 and some of them are better than others.
Yesterday or at some point recently, I lost a YouTube subscriber. It was probably my latest guitar video that displeased them and they left by taking the time to click unsubscribe. My general interests are technology and guitars. Certainly that is driven by my interest in the intersection of technology and modernity. Within that space I enjoy learning about civil society, the public square, artificial intelligence, machine learning, and all things technology related. Outside of that general space I tend to watch videos about guitar pedals and technology. While the two topics are slightly related most people may not share my interest in a mixture of such things. I’m not really worried at all about growing my audience. For the most part the videos that I create and the articles that I write are for my own purposes. They formally help me refine my thoughts and engage in the practice of doing things. Writing dense long misses of prose every day on a weblog is probably not the best method of communication to grow an audience. Some people have found success on Substack writing and sending out newsletters. That is probably directly related to finding a shared community of interest that wants to learn more and engage. Communities of place, circumstance, and interest develop naturally and the ones based on interest are probably the most passionate fan bases for a newsletter.
The other day I was wondering what the actual audience size was for people who are interested in technical track applied machine learning content. My estimate of that audience size is about 10,000 people worldwide that might be actually interested in that topic. At any given time for any given publication a much smaller portion of that base would actually engage and consume that content. We have reached a point in the process of learning about machine learning where the creation of related content outpaces the ability of the community to consume it. That creates islands of specialization and all of the challenges and oddities that are produced from microcosms.
Taking the day off was a great idea. Every now and again having a Wednesday off is great. Part of the day was devoted to taking the AWS certified machine learning specialty beta exam. The whole process took about two hours and was very interesting. The PSI system took a little bit to setup and get running. The actual exam part took about 90 minutes. It has been a bit since my last adventure to a testing facility. It was pretty much exactly the way I remember it being.
It has been some time since I last fell asleep during an epic studying session. It was probably back in graduate school. Those days were fun, but they seem like forever ago. Let me set the state for you a little bit for a moment. My Dell ultra wide monitor was playing some AWS videos on one side and a document was open on the other half of the monitor. My entire focus was on what the presenter was saying then all of a sudden my hands hit the keyboard and my attention jerked back into focus. It had happened all of a sudden. Sure enough for a minute or two I had fallen asleep during some AWS training videos on my day off in the middle of the day. Maybe the right way to look at it is to assume my cup was full and it was time for a nap. Perhaps it was an unplanned power nap. It was most definitely not part of my machine learning and artificial intelligence learning plan for the day. Today was a day that was entirely devoted to learning and studying. That was what made it such an interesting day. It was full of possibility. Oddly enough, it also was a day that that included an impromptu nap.
All of my spare time is being devoted to working on AWS training and certification this week. The one class that really caught my attention was the course, “Seeing Clearly: Computer Vision Theory.” With the announcement of the new AWS DeepRacer and corresponding league it seemed like a good thing to think about and dig into today. Digging in is the fun part of being a lifelong learner. I’m not entirely sure how I feel about the DeepRacer announcement. It is a good method to help build models. It sounds like a very interesting thing to push forward models at an accelerated rate. Seeing what happens will be pretty interesting throughout the next year.
This whole thing looks like it would be fun to do with my first grader. Maybe that would be a good enough reason to buy the racer car and start building models. I really do think this is a method to get people to produce a bunch of distributed models to farm out the work without having to pay top talent. It is a pretty good strategy to get a market edge or to in some ways change the market. People would probably watch this type of racing league.
I just did the “Introduction to Amazon Machine Learning” hands-on demo from Qwiklabs for free. Apparently, Qwiklabs has about 35 free hands on demos that you can do right now for fun to learn and grow your skills. That as you might have already guessed is pretty darn cool and a great opportunity to sharpen your skillset. You can login to Qwiklabs –> Catalog –> Filter: Price –> Free. That should let you check out what hands-on labs might inspire you to start learning today… If that does not inspire you to start digging into the world of machine learning I do not know what will. It is fairly amazing that we have access to free online hands-on labs. Labs are one of the best ways to really see the technology in action. Sure, you can watch a demo, but that type of learning is not the same opportunity. Being able to really dig in and poke around is what makes a hands-on lab so impactful.
You are going to be totally surprised. Yeah – you are probably not going to be surprised at all based on my post yesterday. All of my free time today was spent watching AWS machine learning videos. I really started to dig into those videos and all the content that is now online for free. My honest opinion is that the content from Coursera on the Google Cloud Platform was more dynamic and the combination of constant quizzes and hands-on labs really helped me dig in and absorb the material. However, given that the AWS machine learning content is free and organized pretty well to be highly consumable it works. My plan is to take on every single bit of content they made available. That is about 30 courses and 45 hours of material. The one thing that I have noticed so far is that you can only listen to the content at 1x speed. That might not seem like a very big deal, but normally I listen to lectures in fast forward. That is how I like to go about things. Instead of listening to music in the morning and at night I’m powering through machine learning content. Focusing in on machine learning and improving my skillset has been pretty rewarding.
The AWS training and certification learning library is sorted into domains and a few other filters. Sorting down to the machine learning domain will reduce the learning library to 92 items. At the moment, I have completed 17 of the 92 items. That is not a bad start. I’m not entirely sure how long it will take me to power through all of that content. Some of the items are more involved than others. That is probably a good start toward consuming the whole learning library of 393 items. Some of them looking interesting, but I am willing to bet that the machine learning related items hold my attention better than any of the general items. Based on my recent laser focus on machine learning investing the time to finish the 92 items probably makes sense. They are free and a little bit of training every night is pretty much the path I am electing to walk down.
My thoughts are awash with what might be in the 45 hours of training that Amazon just released. All of my efforts to date have been focused on the Google Cloud Platform certifications, but I think it might be fun to chew through the 30 courses featuring over 45 hours of training that was just released and take the exam. I do enjoy taking exams for some ineffable reason. The idea that the exam is still in beta does make it seem extra shiny. That might just be enough to drive me to the finish line. Being first in the pool is always more fun than having to take the boring post beta version of an exam related to machine learning…
Starting tonight I’m going to tear through these courses and probably write about the process for the next two weeks or so as I absorb the content. The thing that I am the most curious about is which platform I will want to use going forward. I’m super comfortable in the Google Cloud Platform and ready to go do machine learning in that ecotype without reservation. My knowledge of AWS is rather limited. I just spun up an account about fifteen minutes ago.