In this Python Pandas tutorial, you will learn the basics of Pandas by code examples written in Python. If you have zero knowledge of Python, please read this article first.

In the spirit of thanksgiving, let me start by thanking all the active commenters on my blog: you are always very quick to point out typos, flaws and references to literature I overlooked.

We’re back at it in the Proof of Work difficulty spectrum, this time going through how Ethereum’s difficulty changes over time. This is part 4.2 of the part 4 series, where part 4.1 was about Bitcoin’s PoW difficulty, and the following 4.3 will be about jbc’s PoW difficulty.

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Motivation

There’s some advice I’ve read that “when you’ve given the same in-person advice 3 times, write a blog post”. I’ve decided to take that advice and write some of my thoughts on getting a PhD to go into industry.

I finished the Motion Planning course from Robotics this week. It was expected, since the material was quite in line with data structures and algorithms that I have studied during my undergrad. The next one, Mobility, seems to be a notch tougher than Aerial Robotics, mainly because of the focus on calculus and physics (neither of which I have touched heavily in years).

In this post we are going to develop a java recommender application with implicit feedback for an Online Retail. In previous post we developed two java recommender application one for books and one for movies . What is new in this post is that we do not have the luxury of explicit feedback like ratings but rather implicit feedback like for example clicks, time stayed , view times and so on… This is a very common situation when usually the amount of implicit feedback outweighs the amount of explicit feedback(personally guilty on my Amazon purchases).

Looking for a Python job? This article explains Python interview questions and answers them for you. It explains both some Python coding interview questions but also some non-technical questions. This article also contains Python interview questions and answers for testers which can form a source of inspiration.

This was a busy week, I had no time to read anything new, so I’m sharing a note that I wrote for myself, for no other reason than to understand things better. It’s a kind of cookbook of various “transformations” you can apply to a machine learning problem to eventually turn it into something we know how to solve: seeking stable attractors of a tractable vector field.