Onboarding Your Machine Learning Program

Machine Learning’s popularity is continuing to grow and has engraved itself in pretty much every industry. This article contains lessons from a data scientist on how to unlock it’s full potential.


Jupyter is where humans and data science intersect

Discover how data-driven organizations are using Jupyter to analyze data, share insights, and foster practices for dynamic, reproducible data science.


Introduction to Learning Rules in Neural Network

Learning rule is a method or a mathematical logic. It helps a Neural Network to learn from the existing conditions and improve its performance. It is an iterative process. In this machine learning tutorial, we are going to discuss the learning rules in Neural Network. What is Hebbian learning rule, Perceptron learning rule, Delta learning rule, Correlation learning rule, Outstar learning rule? All these Neural Network Learning Rules are in this tutorial in detail, along with their mathematical formulas.


Developing a Deeper Understanding of Apache Kafka Architecture

The Apache Kafka distributed streaming platform features an architecture that – ironically, given the name – provides application messaging that is markedly clearer and less Kafkaesque when compared with alternatives. In this article, we’ll take a detailed look at how Kafka’s architecture accomplishes this.


Understanding what is behind Sentiment Analysis (Part I)

This article doesn’t explain the state of the art of sentiment analysis but the fundamentals of how a computer can learn to infer the polarity of a given document and use it as an excuse to introduce different concepts used in NLP. Thus, no deep technical background is needed.


The case for R, for AI developers

I had a great time this week at the Qcon.ai conference in San Francisco, where I had the pleasure of presenting to an audience of mostly Java and Python developers. It’s unfortunate that videos won’t be available for a while, because there were some amazing presentations: those by Matt Ranney, Mike Williams and Rachel Thomas were particular standouts.
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