This is a tutorial on how to use deep learning to solve the popular MNIST classification problem. There is not a load of innovation happening here; the take away are the pre-processing steps and the tuning of the training process. I have done this with two objectives: Firstly, to get to speed with existing libraries
In this post, I collate, re-organize, and summarize literature relevant to computational advertising today, with the objective of being able to structure problems that warrant data-driven solutions: either in the form of mathematical models, software systems, or business and creative processes. The article is organized as follows: First, for the uninitiated, I provide a brief background
This is a little dated, but posting it FWIW…. PDF If somebody is interested to update and beautify it, will be happy to share the data. Please buzz me for this at firstname.lastname@example.org Thanks!!
At QuaintScience, we co-authored a white-paper with PrediatorDigital, a media company based out of Singapore on the paradigm-shift in analytics from traditional web-analytics platform: Google Analytics and the new, event-based platform: MixPanel. Give it a read here and share your comments.
This is the fourth and final part of a series of posts on “Anti-patterns” in data science. View the previous posts here: 1 2 3 In this (final) post of the series, I will cover a few points to be careful about while building models and drawing inferences and interpretations based on such models. Model
This is the third part of the series of posts covering Anti-patterns in Data Science. Read Part1 and Part2. In this article, I cover a few points to be careful about, during experiment design and data collection. Again, as with the previous two articles, the block diagram describing the “process flow” for solving a problem is reproduced below.
This is the second part of a series of posts that should cover Anti-patterns in Data Science. Read the first part here. In this post, I’ll cover the first set of anti-patterns that on should avoid, or use with utmost care. The discussion will be split along the lines of the various blocks described in
It is imperative to understand what it means to go “Data-Driven” and why and how one should be careful about it. In a series of blogs, I’d like to summarize some learnings I had while navigating the “Data-driven” Quicksand: A wonderland where faulty analysis can lead you to misleading and dangerous interpretations of how world
It is not seldom that I encounter the following question while marketing a solution: “What are the hypothesis you are testing for and how will you prove or disprove it?”, thereby alluding to standard hypothesis testing techniques. In this context, I had a short conversation with a friend a couple of days back on the prevalent culture in many analytics organisations and academic
Data Science in Retail…. Problems to solve.