Emerging applications like machine learning (ML), big data analytics, and artificial intelligence (AI) has created the need for many companies to hire highly skilled and experienced work force. Demand for data scientists, ML engineers and data engineers is booming and will only increase in the next years. The January report from Indeed, one of the top job sites, showed a 29% increase in demand for data scientists year over year and a 344% increase since 2013.
Added by Chris Kachris on May 17, 2019 at 4:50am — No Comments
Machine learning applications require powerful and scalable computing systems that can sustain the high computation complexity of these applications. Companies that are working on the domain of machine learning have to allocate a significant amount of their budget for the OpEx of machine learning applications whether this is done on cloud or on-prem.
Typical machine learning application…Continue
Added by Chris Kachris on May 14, 2019 at 11:30pm — No Comments
Yes, I know, this has been tried a few times and no one listens.... At least not yet. Despite several studies showing otherwise, teams still punt more than they should. Admittedly, some of these studies have been less than rigorous, and often times, assumptions are made that warrant scrutiny (assuming a 50% success rate on all 4th down attempts for example). But I don't think it is the lack of scientific rigor that keeps change at bay. I think the failure to adopt a novel strategy has a lot…Continue
Added by Ray Hall on August 30, 2018 at 9:30am — No Comments
UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! …Continue
This is the 2nd part of the series. Read the first part here: Logistic Regression Vs Decision Trees Vs SVM: Part I
In this part we’ll discuss how to choose between Logistic Regression , Decision Trees and Support Vector Machines. The most correct answer as mentioned in the …Continue
Added by Aatash Shah on November 19, 2015 at 1:00am — No Comments
Classification is one of the major problems that we solve while working on standard business problems across industries. In this article we’ll be discussing the major three of the many techniques used for the same, Logistic Regression, Decision Trees and Support Vector Machines [SVM].
All of the above listed algorithms are used in classification [ SVM and Decision Trees are also used for regression, but we are not discussing that today!]. Time and again I have seen people asking which…Continue
Before you select the best model based on your favorite goodness of fit statistic – Mean Squared Error, Gini, K-S, AUC, or misclassification rate – STOP! Model performance metrics are not a one size fits all measure. As an analyst, selecting the right performance metric might mean the difference between having an exceptionally good result, and having no result.
The classic example: There is only a 3% prevalence of the event of interest in my…Continue
Added by Laura E. Wood Squier on October 24, 2013 at 8:00am — No Comments