There are two forces that are fundamentally remaking the technology landscape today. One is Kubernetes and the other is high performance Object Storage. They are powering (or are shaped by, depending on your perspective) modern, data-rich applications that include AI/ML and application logs. Either way, modern applications need Kubernetes and Object Storage and Kubernetes and Object Storage owe their rise in part to these same modern applications.
They are symbiotic and they are…Continue
Added by Jonathan Symonds on February 14, 2020 at 10:16am — No Comments
Our model for recognizing specific animals in images is a neural network consisting of multiple layers, and the initial layers are already good at understanding the world in general. So instead of “re-inventing the wheel,” we only need to train the final layers.
I was excited to work on a recent project with one of our partners, Wild Detect, because it aligns with one of our goals at Appsilon — to use data science consulting to aid in the…Continue
Added by Michał Frącek on June 25, 2019 at 1:13am — No Comments
About a month ago, I posted a blog on “Technical Deconstruction.” I described this as a technique to break down aggregate data to distinguish between its contributing parts: these parts might contain unique characteristics compared to the aggregate. For instance, I suggested that it can be helpful to break down data by workday - that is to say, maintaining separate data for each day of the week. I said that the data could be further deconstructed perhaps by time period and employee: the…Continue
Added by Don Philip Faithful on April 14, 2018 at 8:00am — No Comments
In recent years, the field of object detection has seen tremendous progress, aided by the advent of deep learning. Object detection is the task of identifying objects in an image and drawing bounding boxes around them, i.e. localizing them. It’s a very important problem in computer vision due its numerous applications from self-driving cars to security and tracking.
Prior approaches of object detection…Continue
Added by Luba Belokon on October 19, 2017 at 8:30am — No Comments
How might a person go about studying something elusive like serial murder or terrorism? I have no formal exposure in this area. Much of the technology that I mention in this blog is meant for another purpose. That other purpose is to study characters in movies, which for me is a great diversion. In particular, I like to map out where certain characters might be found (or lost - i.e. missing characters): the settings they occupy, their roles, their relationships. It goes without saying…Continue
Added by Don Philip Faithful on October 9, 2017 at 5:00am — No Comments
In order to prevent my programs from freezing up while running long calculations, I generally run the calculations on separate threads. In Java, this process can be accomplished by separating the GUI from processing. In the code below, a thread for an instance of MyProcessing would be invoked using start(): e.g. “(new MyProcessing()).start();” would run indefinitely until T is made null. T can be made null by calling stop() or by directly making T null. Often when the GUI is closing, I…Continue
Added by Don Philip Faithful on March 25, 2017 at 9:42am — No Comments
Last Sunday at Trivadis Tech Event, I talked about R for Hackers. It was the first session slot on Sunday morning, it was a crazy, nerdy topic, and yet there were, like, 30 people attending! An emphatic thank you to everyone who came!
R a crazy, nerdy topic, - why that, you'll be asking? What's so nerdy about using R?
Well, it was about R. But it was neither an introduction ("how to get things done quickly with R"), nor was it even about data science. True, you…
Added by Sigrid Keydana on March 24, 2017 at 2:30am — No Comments
Probably like most people, I tend to recognize data as a stream of values. Notice that I use the term values rather than numbers although in practice I guess that values are usually numerical. A data-logger gathering one type of data would result in data all of a particular type. Perhaps the concept of “big data” surrounds this preconception of data of type except that there are much larger amounts. Consider an element of value in symbolic terms, which I present below: there is an index such…Continue
Added by Don Philip Faithful on December 10, 2016 at 9:30am — No Comments
A theme in my blogs is how the "structure" of data - rather than just the "content" - affects what that data can say and is capable of doing. In particular, I suggest that certain structures tend to reinforce certain contents; this means that a structural imposition can have an effect similar to a contextual imposition. Structure is an interesting conversation…Continue
Added by Don Philip Faithful on October 22, 2016 at 5:30am — No Comments
Like many students about to finish their undergraduate degree, I decided to artificially inflate my grades by taking some "bird courses." These are not courses about birds. Other students assured me that the courses were designed to bolster my marks and to help me complete my program requirements. Considering the many bird courses available, I decided to take introductory music, which was essentially a history course focused on music. It required a lot of…Continue
Added by Don Philip Faithful on December 6, 2014 at 8:52am — No Comments
Given the nature of the community, presumably many visitors already have a strong understanding of the nature of quantitative data. Perhaps more mysterious is the idea of qualitative data especially since it can sometimes be expressed in quantitative terms. For instance, "stress" as an internal response to an externality differs from person to person; yet it would be possible to canvas a large number of people and express stress levels as an aggregate based on a perceptual gradient: minimal,…Continue
Added by Don Philip Faithful on October 25, 2014 at 6:37am — No Comments
Up to about 1999 web search…Continue
Added by William Vorhies on September 12, 2014 at 8:26am — No Comments
Here’s a brilliant presentation from Mike Bowers, Principal Engineer at the Church of Jesus Christ of Latter Day Saints. It accomplishes two major objectives; Mike reviews the strengths and weaknesses of the five major classes of databases today (relational, dimensional, object, graph and document). He then dissects the major NoSQL databases on the market including MarkLogic, Mongo, Riak, Cloudant/Couch DB and Cassandra. How do they stack up? Are they enterprise ready? If developer…Continue
Added by Tony Agresta on January 4, 2013 at 10:20am — No Comments