It is getting late in the day for me. I am a believer in deep work so several months ago I decided to get up early morning and study for a couple hours. So far, it has worked very well for me. I have set a wakeup alarm for 05:00 AM 7-days a week. Typically I am awake between 04:30 and 05:00 so I just turn off the alarm and head down to my home office. Around 05:00 PM I feel the work day coming to an end and am ready to punt. I use EyeDefender to break every two hours. During my 5 to 10 minute breaks I walk and get some fresh water. What can I say; it works for me. On weekends I tend to just do two are three blocks. On work days I tend to do five.
The title for this post ACM & Coursera are just topics that I listed so I could write about two specific events. Perhaps each could have used a blog entry, but I am about to be done for the day. To learn more about the ACM read here.
Last week I attended an Association for Computing Machinery (ACM) webinar. Every Special Interest Group (SIG) organizes some webinars during the year. In general they are always interesting. I follow a few SIGs. Last week there was an interesting webinar and Andrew Ng was the guest speaker. I have read several articles regarding Andrew’s work but had not seen him in an online presentation. Seems like he is very intelligent and a simple person. I enjoyed the webinar. I would guess there more webinar attendees than usual. I have not seen the statistics yet.
Andrew is interested in educating the masses so he co-founded Coursera. During his presentation he brought the online courses platform several times. You can read more about Andrew Ng here. You can read more about Coursera here.
I used to purchase and read many technical books. Some of them I use as a reference while others sit dormant in my office resting in a shelf or on the floor. Every time I open a new book, I write my name and year. I then tend to read them cover to cover spending some time experimenting with the subject matter. Have done this for at least a couple decades. What I figured out is that after reading end experimenting, how I could validate what I have just learned. On some few occasions I have applied the newly acquired knowledge to a project, but unless you work for a very large company, and are allowed / able to change departments every year or so, it is hard to apply new concepts and verify you actually learned the subject.
In order to address this urge, I started taking courses on Coursera. I finished a set of five courses which constitute a specialization on Big Data. Lectures are followed by graded quizzes and assignments. If you pass, then you must have learned something. So far I have learned a lot and have had a chance to experiment with technologies that I knew they existed but so far have not had the opportunity to do so (e.g., Neo4j, Spark, KNIME among others). The specialization touched on Machine Learning (ML) but was not enough. I want to learn more. This was one of the reasons I attended the ACM webinar. It also coincided that this weekend I finished the Big Data specialization and felt I was ready for the next specialization. I have enrolled, and I am taking the first course in ML taught by Andrew Ng. He makes ML sound simple and accessible to anyone that wants to learn. Will let you know my findings next year when I complete the ML specialization. I believe there is a more advanced one which I will take next.
Let’s see if I can post this entry and then spend a few minutes registering my AWS DeepLens camera. Seem to be having a lot of trouble registering the unit. That said, Amazon is helping me out. When the camera becomes operational will write a post in this blog sharing my adventure :o)
If you have comments or questions, please leave them below. I will address them as soon as possible.
In the meantime, happy learning;
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