Following on my previous WTF post on Machine Learning, it just make sense to continue in this line of thought to address another of many popular and trendy concepts. We are talking about: Deep Learning.
So without further due, lets explain WTF is deep learning shall we?
Simply put, and as inferred from the previous post mentioned, deep learning is one of now many approaches to machine learning we can find out there, along the lines of other approaches like decision tree learning, association rule learning, or Bayesian networks.
While deep learning is not new, was introduced by Dr. Rina…
According to Encyclopedia Britannica, artificial intelligence (AI) can be defined as:
“The ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, like the ability to reason, discover meaning, generalize, or learn from previous experiences.”
By now, we have all heard about how AI can make it possible for computers, machines and other electronic devices to perform increasingly complex and human-like tasks.
As all this sounds almost like magic, with machines performing increasingly complex tasks…
In a world where we might think is being ruled and controlled by tech geeks and data scientists, during meetings and phone calls with customers I’m still, often, being hit with honest and candid questions about any given topic about the data and analytics and give my personal take on them. In virtue of this, I’ve decided to take a shot and a series of posts to answer, as plainly as I possibly can, common questions I receive in my day-to-day life as a consultant and analyst.
Starting with my most popular question nowadays: WTF is machine learning?
As mentioned right at the end of my Look Back Into 2017 And Forward To 2018 post, I did start this year looking forward for an exciting 2018 and, well, it seems my dream has coming true.
As usual, this briefing was a great opportunity to know what the company is and what its up to, in the present and for the future. …
As the need for gathering data continues, organizations keep dealing with increasing amounts of information that need to be stored, processed, and analyzed faster and better, stimulating the growth and evolution of the high performance computing (HPC) market.
One key segment in this market the continues to grow, especially in recent years, is the high performance data analytics (HPDA) as organizations continue to adopt and evolve their big data and data lake initiatives, to the point that IDC forecasts that in 2018, the HPDA server market will reach $2.6 billion (23.5% CAGR) and the HPDA external storage market will add…
Still distilling good results from the acquisition of former consultancy company Think Big Analytics, Teradata, a powerhouse in the data management market took one step further to expand its data management stack and to make an interesting contribution to the open source community.
Fully developed by the team at Think Big Analytics, in March of 2017 the company launched Kylo –a full data lake management solution– but with an interesting twist: as a contribution to the open source community.
Offered as an open source project under the Apache 2.0 license Kylo is, according to Teradata, a new enterprise-ready data lake…
“Companies are looking for a unified and open approach to help them accelerate and expand the flow of data across their data landscapes for all users.
SAP Data Hub bridges the gap between Big Data and enterprise data, enabling companies to build applications that extract value from data across the organization, no matter if it lies in the cloud or on premise, in a data lake or the enterprise data warehouse, or in an SAP or non-SAP system.”
Not long ago I had the opportunity to read a book from my long reading list.
“Thank You for Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations” is a book written by famous author and journalist Thomas L. Friedman and logically as you might know, a best-seller.
Admitting I have a mild tendency to avoid best-sellers ―I’ve ran into some disappointments when reading them― I was a bit reluctant to its reading, especially because this was, according to the back cover, close to things I’m familiar with being an industry analyst and consultant in the technology…
As we inevitably approach the end of the year ーa year marked with many important advances in all areas of the data management spaceー I just can’t avoid thinking with expectation and excitement what should be there just around the corner for 2018. If 2017 was all but boring, 2018 looks like another promising one, no less fast and competitive than 2017.
Prediction is very difficult, especially if it’s about the future.
I will avoid making big prediction statements and instead, take a look at some relevant things happened this ending year and…
Industry Analyst & proud father of 3. Opinions are mine. Montréal, Québec