How Big Data is used in Recommendation Systems to change our lives
A Recommendation systems have impacted or even redefined our lives in many ways. It works in well-defined, logical phases which are data collection, ratings, and filtering.
on Oct 30, 2015 in Amazon, Big Data, Kaushik Pal, Recommendations, Recommender Systems
Integrating Python and R, Part 2: Executing R from Python and Vice Versa
The second in a series of blog posts that: outline the basic strategy for integrating Python and R, we will concentrate on how the two scripts can be linked together by getting R to call Python and vice versa.
on Oct 30, 2015 in Python, Python vs R, R
Integrating Python and R into a Data Analysis Pipeline, Part 1
The first in a series of blog posts that: outline the basic strategy for integrating Python and R, run through the different steps involved in this process; and give a real example of how and why you would want to do this.
on Oct 29, 2015 in Data Analysis, Mango Solutions, Python, Python vs R, R
Data Science of IoT: Sensor fusion and Kalman filters, Part 1
The Kalman filter has numerous applications, including IoT and Sensor fusion, which helps to determine the State of an IoT based computing system based on sensor input.
on Oct 29, 2015 in FutureText, IoT, Kalman Filters, Sensors
Random vs Pseudo-random – How to Tell the Difference
Statistical know-how is an integral part of Data Science. Explore randomness vs. pseudo-randomness in this explanatory post with examples.
on Oct 26, 2015 in Correlation, Random
Recurrent Neural Networks Tutorial, Introduction
Recurrent Neural Networks (RNNs) are popular models that have shown great promise in NLP and many other Machine Learning tasks. Here is a much-needed guide to key RNN models and a few brilliant research papers.
on Oct 7, 2015 in Deep Learning, Neural Networks, NLP, Recurrent Neural Networks
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