|Data Science Central Weekly Digest, 23 Nov…|
Added by Kurt A Cagle on November 23, 2020 at 7:30pm — No Comments
The model-free, data-driven technique discussed here is so basic that it can easily be implemented in Excel, and we actually provide an Excel implementation. It is surprising that this technique does not pre-date standard linear regression, and is rarely if ever used by statisticians and data scientists. It is related to kriging and nearest neighbor interpolation, and apparently first mentioned in 1965 by Harvard scientists working on GIS (geographic information systems). It was referred…Continue
Added by Vincent Granville on November 23, 2020 at 6:00pm — No Comments
In my previous post, What’s Driving the Future of Work and Professions?, I mentioned PESTLE as a helpful framework for classifying external forces that act on all of us and the companies for which we work. In this article, I present a list of them for you to consider. It’s not encyclopedic, but it’s a good…Continue
Added by Howard M Wiener on November 23, 2020 at 6:00am — No Comments
Being an expert at developing and understanding ML, or Machine Learning algorithms, takes time and a lot of hard work. That’s why ML (machine learning) engineers are been seen constantly learning while at the job. If the learning stops, your professional growth stops. Many of us, especially the AI aspirants, think that watching tutorial videos on AI (artificial intelligence) modeling or ML algorithm development on YouTube…Continue
Added by Aileen Scott on November 22, 2020 at 7:30pm — No Comments
I’ve been reading the interesting and soul-searching (from a data scientist perspective) book from Cathy O’Neil titled “Weapons of Math Destruction”, or WMD as used in the book. The book provides several real-world examples of how Big Data and Data Science – when not properly structured – can lead to ethically-wrong unintended consequences.
Added by Bill Schmarzo on November 22, 2020 at 11:20am — No Comments
Added by Kurt A Cagle on November 20, 2020 at 7:30am — No Comments
Over the years, I've had people ask me how a taxonomy differs from an ontology. The answer (or at least a reasonably simple answer) is that "a taxonomy is a tree shaped ontology".
It is worth digging bit deeper to understand what that means, however:
Way back in the early 18th century, a Swedish biologist by the name of Carl Linnaeus…Continue
Added by Kurt A Cagle on November 19, 2020 at 7:42pm — No Comments
Mobile applications based on machine learning are reshaping and affecting many aspects of our lives. Implementing machine learning on mobile devices faces various challenges, including computational power, energy, latency, low memory, and privacy risks. In this article, we…Continue
Added by AI on November 19, 2020 at 11:00am — No Comments
This article was written on OpenAI.
We’ve created images that reliably fool neural network classifiers when viewed from varied scales and perspectives. This challenges a claim from last week that self-driving cars would be hard to trick maliciously since they capture images from multiple scales, angles, perspectives, and…Continue
Added by Andrea Manero-Bastin on November 19, 2020 at 1:59am — No Comments
It is becoming increasingly common for organizations to collect very large amounts of data over time, and to…Continue
Added by Sharmistha Chatterjee on November 19, 2020 at 12:22am — No Comments
Summary: Let’s start by clarifying the difference between RPA (Robotic Process Automation) and IA (Intelligent Automation). Then we’ll show why AI/ML inside Intelligent Automation is the secret sauce that really makes this work.
Added by William Vorhies on November 18, 2020 at 9:00am — No Comments
As you start incorporating machine learning models into your end-user applications, the question comes up: “When is the model good enough to deploy?”
There simply is no single right answer.
There is no clear-cut measure of when a machine learning model is ready to…
Added by Henrik Skogström on November 18, 2020 at 5:30am — No Comments
If are you an established DevOps engineer, or do you plan to enter DevOps?. Forrester, the leading consulting firm, labeled 2018 as 'the year of enterprise DevOps' and predicts DevOps being adopted by 50 percent of companies all over the world.
DevOps learning calls for constant commitment, desire and enthusiasm. The DevOps training will take between one week and several…
The educational sector had already been gradually embracing technology when the coronavirus came along. This substantial change has also accelerated the industry’s efforts to evaluate and embrace newer technologies. It offers the potential to lend assistance further and aid their many efforts to deliver quality education. This search has led to the emergence…Continue
Added by Ryan Williamson on November 17, 2020 at 8:00pm — No Comments
Added by Kurt A Cagle on November 17, 2020 at 6:30am — No Comments
The varying needs of patients and the growing need for better management of healthcare have driven companies to look for answers and help from technology. Suffice it to say that technology answered the call and how! While there are plenty of technology-driven resources that offer quality help. None has proven to be quite as potent as the Internet of Things, which is essentially a network of devices connected via the internet. In the context of healthcare, it empowers healthcare providers…Continue
Added by Ryan Williamson on November 17, 2020 at 2:08am — No Comments
Meet two text mining experts in today’s interview, which explores some of the common issues faced by data scientists in text analytics. Prof. Dursun Delen and…Continue
Added by Rosaria Silipo on November 15, 2020 at 12:00am — No Comments
After a few biostatistics classes, I began fitting my first logistic regression model using my physician friend’s data on tumors excised from skin cancer patients. I realized that although we were very clear about the dependent variable we were trying to predict – a certain feature of the tumor – I really did not know how to pick the independent…Continue
Image Credits: Unsplash
I was recently invited to judge a Data Science competition. The students were given the ‘heart disease prediction’ dataset, perhaps an improvised version of the one available on Kaggle. I had seen this dataset…Continue