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In the information era, data is no protracted scarce, on the other hand, it is irresistible. From delving into the overpowering quantity of data to precisely interpret its complexity in order to provide insights for intense progress to organizations and businesses, all sorts of data and information is exploited at their entirety and this is where statistical data analysis has a significant part.
Markov chain Monte Carlo (MCMC) is a powerful class of methods to sample from probability distributions known only up to an (unknown) normalization constant. But before we dive into MCMC, let’s consider why you might want to do sampling in the first place.
We present a brief history of the field of interpretable machine learning (IML), give an overview of state-of-the-art interpretation methods, and discuss challenges. Research in IML has boomed in recent years. As young as the field is, it has over 200 years old roots in regression modeling and rule-based machine learning, starting in the 1960s.
In this article we’ll take performance of the same SSD300 model even further, leaving Python behind and moving towards true production deployment technologies.
Artificial intelligence is a broad and dynamic field that can quickly overwhelm a beginner. The GitHub project "AI Expert Roadmap" aims to give learners an idea of the landscape and guide them on what to learn next. Using these roadmaps, they should develop some understanding of why one tool is better than another in some cases and learn to remember that hip and trendy never means best suited for the job.