Generative Adversarial Networks (GANs) Explained in Layman Terms
NIPS2016 (Neural Information Processing System) is an annual event that attracts the best and the brightest of the field of Machine Learning both from academia as we...
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The Perceptron Algorithm explained with Python code
1. Introduction Most tasks in Machine Learning can be reduced to classification tasks. For example, we have a medical dataset and we want to classify who has diabetes (po...
ALDI – A New Paradigm for Integrating Marketing Analytics with Data Science
Owing to the data deluge and the Cambrian explosion of machine learning techniques over the past decade, one might have expected the transformation of marketing strategy ...
Where are developers looking next?
As part of the research underpinning Developer Economics we actively monitor industry trends and opportunities, looking for new areas of significant developer interest. I...
What are the Big Guys Using?
Summary: The largest companies utilizing the most data science resources are moving rapidly toward more integrated advanced analytic platforms. The features they are ...
Top 30 Data Science Articles of the Year
As we approach 2017, we have compiled a list of the most popular data science, machine learning, deep learning and related articles published on DSC in 2016. Many great a...
Preambles and Future Directions
I will be using this blog to assemble a number of different concepts that I introduced over many years in previous blogs (indicated in bold); then I will explain where al...
Dark Data: The billion dollar opportunity
Every organization collects, stores and retains portions of dark data. It’s the digital equivalent of emotional baggage which hangs around after every user interaction,...
Great list of resources: data science, visualization, machine learning, big data
Fantastic resource created by Andrea Motosi. I’ve only included the 5 categories that are the most relevant to our audience, though it has 31 categories total, incl...
A Beautiful Probability Theorem
We all know that, given two events A and B, the probability of the union A U B is given by the formula P(A U B) = P(A) + P(B) – P( AB) where AB represents the int...