apatry
Deep Work by Cal Newport gave me a way to think about my time management: information work is not the same as a factory line where doing the same thing at similar productivity from 9 to 5 makes sense, and it is important to dedicate long stretch of quality time to be productive (vs busy).
There are no silver bullets, but learning what worked for a group of people, testing it for myself, adapting it, and using it as needed has been helpful to me.
We can debate what off-the-chart means, but when we cross-reference the states with the highest gun death per capita (https://www.cdc.gov/nchs/pressroom/sosmap/firearm_mortality/...) with the states that are the most religious (https://www.pewresearch.org/fact-tank/2016/02/29/how-religio...), we find a lot of overlap.
Correlation is not causation, but I can't see how we can claim these two are negatively correlated.
Google invested in the fundamental from the start and they pushed the limit of what search engines can do.
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The main advantages of mallet over weka (the main java toolkit used in academic machine learning) for Natural Language Processing are:
- No need to map words and features to position in a feature vector yourself.
- Instances preprocessing can be defined in pipes that can be saved along the models. So no need to remember the pre-processing of data for each experiments.
- Contains algorithms for structured learning (CRF, HMM and general graphic models).
On the other hand, Mallet implements less algorithm (e.g. no Support Vector Machines to my knowledge).
In short, it is a nice toolkit to be aware of if you are planning to do Natural Language Processing.