PART 1: Comprehensive Knowledge Archive Network (CKAN)

CKAN(Comprehensive Knowledge Archive Network) is an open-source data management system designed to power data hubs and portals. CKAN facilitates the worldwide publication, sharing and use of data. Nowadays, each person carries around 5 GB of data in total stored data around the world. This increases the demand for platforms like CKAN that enables people to access the necessary data sets across the world. CKAN is a powerful, open source data management system that makes data easily accessible by providing tools for streamlining, publishing, and sharing data across multiple users from different domains. This means that without any license fees we…

Let’s say your data is scaled properly by using either of the following techniques: Standardization where scaled values are centered around the mean with a unit standard deviation. This means that the mean of the attribute is zero and the resultant distribution has a unit standard deviation. Normalization where values are shifted and rescaled so that they end up ranging between 0 and 1 which is also known as Min-Max scaling.

The model is considered accurate when it operates on training and test data with the highest precision in exactly the same way. …


Predicting unknown from known is a classical way of how machine learning works and where the basic operation of the recommendation system begins. Many recommendation systems operate on three principles.

(i) Editorial or hand curative- list of favorites, staff picked, essential items. problem : No input from User

(ii) Simple Aggregation- Top 10 sellings, most recent favorite, most popular. problem : Not to individuals choice.

(iii) Tailored to Individual Users- Understanding a user’s taste, and then suggesting his / her taste product. Ex: Recommendations on Amazon.

Finding the taste of individual users is a challenging task for any recommendation system…

“In the long history of humankind, those who learned to collaborate and improvise most effectively have prevailed” -in commemoration of Charles Robert Darwin on the ‘Darwin Day’.

In the ‘Computational Intelligence’ domain, we have to consider a situation in which we learn and characterize the uncertainty for decision making. The problem is how to balance between exploring and exploiting while taking decisions and making computations.

Splitting your computational time into ‘Exploration’ (learning) and ‘Exploitation’ (optimal decisions) is a challenging task, which is the same as to find the most delicious ice-cream you can go to every ice-cream parlor in your city (exploration) and try every ice-cream flavor at every parlor(exploitation). The equilibrium position in the tradeoff also depends on the optimization strategy for the problem and data…

Digvijay Mali

Graduate Student at Viterbi School of Engineering | University of Southern California, Los Angeles.

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