Zsh is particularly powerful when you need some good old expansions. It supports common ones used by Bash too, and add many flags and modifiers on top. It comes in handy to manipulate quickly your commands without writing boring shell scripts.
Source: A Guide to Zsh Expansion with Examples, an article by Matthieu Cneude.
I have developed a specific process for myself that I follow when applying a neural net to a new problem, which I will try to describe.
Source: A Recipe for Training Neural Networks, an article by Andrej Karpathy.
Because microprocessors are so fast, computer architecture design has evolved towards adding various levels of caching between compute units and the main memory, in order to hide the latency of bringing the bits to the brains. However, the key insight here is that these caches are partially shared among the CPUs, which means that perfect performance isolation of co-hosted containers is not possible. If the container running on the core next to your container suddenly decides to fetch a lot of data from the RAM, it will inevitably result in more cache misses for you (and hence a potential performance degradation).
Source: Predictive CPU isolation of containers at Netflix, an article by Benoit Rostykus and Gabriel Hartmann.
In the last post of this series we introduced trying to model fire probability in Northern California based on weather data. We showed how to use SQL to do data shaping and preparation. We ended with a data set that was ready with all the fire occurrences and weather data in a single table almost prepped for logistic regression.
There is now one more step: sample the data. If you have worked with logistic regression before you know you should try to balance the number of occurrences (1) with absences (0). To do this we are going to sample out from the non_fire_weather equal to the count in fire_weather and then combine them into one table.
Source: Using PostgreSQL and SQL to Randomly Sample Data, an article by Steve Pousty.