Machine Learning and Artificial Intelligence are two terms that are used interchangeably all the time. So, are AI and ML the same? Most definitely not!
Any technology that makes a system exhibit human-like intelligence is AI. Machine Learning is actually one type of AI. Machine Learning makes decisions by relying on the use of mathematical models that are trained on data. ML models are capable of making better decisions when more data is available.
For the last seven years of my career, I have been living the Agile methodology. Needless to say, participating in the periodic ceremonies have become commonplace for me.
Following the example I created when my 100th article on DZone was published on April 5, 2017, my 200th article for DZone will be a retrospective of my last 99 articles – spanning from April 13, 2017 through August 24, 2017. I plan to cover what fared well with DZone readers and what failed to connect to the same audience.
Recently I was teaching an overview class for new Scrum Masters. I was covering the five important events (meetings) in Scrum and had just introduced the Daily Standup Meeting (DSM), when a learner interrupted with the following question: “Given the cost to people’s work time and the cost to the corporation, do Scrum teams generally feel there is value in the Daily Standup Meeting?” He followed with, “How do you feel about the Daily Standup Meeting?”
A study was conducted at the University of Oslo Norway (V. Stray et al, 1997) to answer the first question. The method of study was a survey of professional software developers. Those conducting the survey received 221 responses from professionals who identified either as a general computer programmer or a web developer. Participation was voluntary, no compensation was given, and controls were placed to prevent the same respondent from answering more than once.
"Coaching works because it’s all about you. When you connect with what you really want and why — and take action — magical things can happen." Emma-Louise Elsey
What can we do to prepare organizational culture for Agile transformation? In one of the brainstorming sessions, this idea was coined and we started elaborating it.
This thought came in a conversion with a couple of leaders where they are asking how we can rapidly do something to can change the organizational culture, the DNA of the people.
You can find all .Net core posts here.
Yesterday, I was going through different articles as usual and one site attracted my attention.
We keep hearing about new solutions for test automation and continuous testing. Such solutions aim to increase the test automation authoring as well as the maintenance associated with these tests as the product evolves.
With this trend, many software quality engineers, SDET, and test automation architects are asking themselves whether their job is at risk and what the future holds for them.
This is the fifth in a series of posts exploring Scrum Mastery. In our first post, we introduced the 4 dimensions of Scrum Mastery: Team Identity, Team Process, Product Value, and the Organization. In this post, we will explore the Organization dimension.
How is the organization enabling you to maximize the benefits of Scrum? How is the organization holding you back?
"I don’t care about the tech — what I really want to hear is how this product fits in our processes and helps our people get more done."
That was the message my co-founder and I heard from an executive at a major bank last week. For us, it was both a deja vu and a major relief because we’d just presented at the Cablelabs Summer Showcase about the importance of aligning people, process, and technology together. The executive was pleased about how RackN had achieved that balance.
Finding a piece of software for creating a quick prototype of your future application may seem like a trivial task. But software prototypes are created by different people for different reasons. It could be a business analyst trying to communicate her vision to a designer, a marketer working on a website with a web dev, or a project manager demonstrating a future mobile app to a client.
Since people use wireframing tools for different purposes, it would be nice to know which tool would be your best bet for your specific situation.
Previously, we saw some of the very basic image analysis operations in Python. In this last part of basic image analysis, we’ll go through some of the following contents.
The following contents are the reflection of my completed academic image processing course in the previous term. So, I am not planning on putting anything into the production sphere. Instead, the aim of this article is to try and realize the fundamentals of a few basic image processing techniques. For this reason, I am going to stick to using SciKit-Image – numpy mainly to perform most of the manipulations, although I will use other libraries now and then rather than using most wanted tools like OpenCV
