Architecture of Convolutional Neural Networks (CNNs) demystified

I will start with a confession – there was a time when I didn’t really understand deep learning. I would look at the research papers and articles on the topic and feel like it is a very complex topic. I tried understanding Neural networks and their various types, but it still looked difficult. Then one day, I decided to take one step at a time. I decided to start with basics and build on them. I decided that I will break down the steps applied in these techniques and do the steps (and calculations) manually, until I understand how they work. It was time taking and intense effort – but the results were phenomenal. Now, I can not only understand the spectrum of deep learning, I can visualize things and come up with better ways because my fundamentals are clear. It is one thing to apply neural networks mindlessly and it is other to understand what is going on and how are things happening at the back. Today, I am going to share this secret recipe with you. I will show you how I took the Convolutional Neural Networks and worked on them till I understood them. I will walk you through the journey so that you develop a deep understanding of how CNNs work. In this article I am going to discuss the architecture behind Convolutional Neural Networks, which are designed to address image recognition and classification problems. I am assuming that you have a basic understanding of how a neural network works. If you’re not sure of your understanding I would request you to go through this article before you read on.


SQL for Data Analysis – Tutorial for Beginners – ep3

Today I’ll show you the most essential SQL functions, that you will use for finding the maximums or the minimums (MAX, MIN) in a data set and to calculate aggregates (SUM, AVG, COUNT). Then I’ll show you some intermediate SQL clauses (ORDER BY, GROUP BY, DISTINCT) as well, that you have to know to be able to use SQL for data analysis efficiently! And this is gonna be super exciting, as we will still use our 7M+ lines data set!


How R Powers Data Science at Microsoft

In this video “How R Powers Data Science at Microsoft” from the EARL 2017 conference in San Francisco (June 5-7, 2017), insideBIGDATA’s Managing Editor and resident data scientist Daniel D. Gutierrez chats with Vijay K. Narayanan – Director, Algorithms and Data Science Solutions, Microsoft. Discussion topics include: Microsoft’s continued commitment to the R language, recent developments in AI at Microsoft, Vijay’s co-authored article appearing in the Harvard Business Review – “Where Predictive Analytics is having the Biggest Impact,” as well as Microsoft’s future directions.


Text Clustering : Get quick insights from Unstructured Data 1

In this two-part series, we will explore text clustering and how to get insights from unstructured data. It will be quite powerful and industrial strength. The first part will focus on the motivation. The second part will be about implementation.
This post is the first part of the two-part series on how to get insights from unstructured data using text clustering. We will build this in a very modular way so that it can be applied to any dataset. Moreover, we will also focus on exposing the functionalities as an API so that it can serve as a plug and play model without any disruptions to the existing systems.
•Text Clustering: How to get quick insights from Unstructured Data – Part 1: The Motivation
•Text Clustering: How to get quick insights from Unstructured Data – Part 2: The Implementation


Tackling the limits of deep learning

Richard Socher explains how Salesforce is doing the heavy lifting to deliver scalable AI to customers.


Cross-Fitting Double Machine Learning estimator

In a late post I talked about inference after model selection showing that a simple double selection procedure is enough to solve the problem. In this post I’m going to talk about a generalization of the double selection for any Machine Learning (ML) method described by Chernozhukov et al. (2016).


Linear Models, ANOVA, GLMs and Mixed-Effects models in R

As part of my new role as Lecturer in Agri-data analysis at Harper Adams University, I found myself applying a lot of techniques based on linear modelling. Another thing I noticed is that there is a lot of confusion among researchers in regards to what technique should be used in each instance and how to interpret the model. For this reason I started reading material from books and on-line to try and create a sort of reference tutorial that researchers can use. This post is the result of my work so far.
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