Showing posts from January, 2019

8 Machine Learning Algorithms explained in Human language

What we call “Machine Learning” is none other than the meeting of statistics and the incredible computation power available today (in terms of memory, CPUs, GPUs). This domain has become increasingly visible important because of the digital revolution of companies leading to the production of massive data of different forms and types, at ever increasing rates: Big Data. On a purely mathematical level most of the algorithms used today are already several decades old. In this article I will explain the underlying logic of 8 machine learning algorithms in the simplest possible terms. I. Some global concepts before describing the algorithms 1. Classification and Prediction / Regression Classification Assigning a class / category to each of the observations in a dataset is called classification. It is done a posteriori, once the data is recovered. Example: classifying consumers reasons of visit in store in order to send them a personalized campaign. Prediction A prediction is made on a new observ…

Deep Learning Architectures explained in Human Language

“Deep” Learning has attracted much attention during these past years. And for a good reason: this subset of machine learning has stood out impressively in several research fields: facial recognition, speech synthesis, machine translation, and many others. These research fields have in common to be perceptual problems related to our senses and our expression. They have long represented a real challenge for researchers because it is extremely difficult to model vision or voice by means of algorithms and mathematical formulas. As a result, the first models that have been implemented in these fields have been constructed with a good deal of business expertise (in speech recognition: decomposition into phonemes, in machine translation: application of grammatical and syntactic rules). Years of research have been dedicated to the exploitation and processing of these non-structured data in order to derive meaning. The problem is that these new representations of data invented by researchers have…