Tuesday, June 19, 2018

Design Fundamentals from NID NODE

I had registered for Design Fundamentals course from National Institute of Design's NODE program last year. It took quite some time to actually finish the course as I was caught up in many things. As the title suggests, the course is more on theory aspects of design. The course amount has almost doubled now, nevertheless, it's worth it. Of the many lessons, I think Analytical Drawing is cool and so is Composition. And among the background music, I liked Stop - Ghost K (cheesepuff piano remix) the most. If one is already a design practitioner, this course helps as a refresher.

Monday, June 18, 2018

SimpleMind for Mind Mapping

SimpleMind is an amazing mind mapping software. An example of a mind map created using SimpleMind is given below.

SimpleMind updates their apps for macOS and iOS with new feature and enhancements. iOS app can sync in local networks with macOS app without having to sync with a third-party cloud service. This does not work with Android app however. So I guess they use bonjour protocol.

I have looked into other mind mapping software, but I find this app to be more simple, powerful and robust. The UI is smooth and the mind maps produced are even more nice. The themes, customization are also rich enough that I don't miss anything that a mind mapping software requires.

Mind mapping is a visualisation tool for those who prefer having ideas noted down that way. The mobile app helps to quickly brainstorm, visualise ideas and at the end we can see the whole interconnection of topics, which is lot easier to keep in mind than reams and reams of texts.

The .smmx is a proprietary file format for storing SimpleMind mind maps. We can export in other formats like OPML, but it will lose any rich content information, in case one needs to use another app. But I don't see a need for that anyway.

Conference Summary - Building Data products at Uber

This is my summary of HasGeek Open House conference on Building Data Products at Uber, by Hari Subramanian held on 15th this month.

1. Data size is in petabytes.
2. Results found in staging is not quite the same when using the same model in production due to various factors.
3. For deep learning, tensor flow is used. Results found in AWS and GCP are different.
4. They have build their own BI tools for visualisation.
5. Hive is extended in-house. Hive and Spark overlaps to a certain extend. There are few map-reduce jobs still used which is why Hive is used.
6. Uses own datacenter.

The talks was a high level overview of how Uber uses ML.