Articles

(Jan 20) #sql #optimization

The main benefit of memory-optimized tables are that rows in the table are read from and written to memory which results in non-blocking transactions at super-fast speed. This is especially useful for applications such as high volume online transaction processing systems. In this article, Denis Gobo tests the performance improvement of memory optimization offered in Micrsoft's SQL Server by creating two databases, one vanilla the other memory optimized, and measuring the time taken to push to and query data from both.

(Jan 20) #rust

A memory allocator is one of the fundamental building blocks of an operating system, since it's responsible for allocating memory resources for programs to run. But how do you go about making a malloc system? Well that's the question Philipp Oppermann answers in this extensive article, where he implements three different types of memory allocators in Rust: a bump, linked list, and fixed-size block allocators. Furthermore he discusses the strengths and limitations of each type of design.

(Jan 27) #ai #machine learning

Deep Q Network (DQN) is a deep reinforcement machine learning technique that combines Q-learning with two neural networks; one to calculate the Q-Value in the state St and the second to calculate the Q-Value for state St+1. In this extensive article by Nikola M. Zivkovic, gives a brief introduction deep reinforcement learning, explains Q-Learning vs Double Q-Learning and DQN and Double DQN. He ties it all together with two implementations; one using TensorFlow 2 and the second with TF-Agent.

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