Have you looked at LSM k-v stores (RocksDB being the obvious one)? Under the hood they are based on building immutable layers of data that are implicitly merged. Clones that share data are cheap (check out rocksdb-cloud…
The pitch is faster, and more space efficient since column stores are far better for analytics than row stores. Some benchmarks that found ~5-10x speedup: https://uwekorn.com/2019/10/19/taking-duckdb-for-a-spin.html…
Sorry, I should've been clearer! Beginner to ML? Stay away from SVMs. This tutorial looks good, and well written.
Agreed -- linear SVMs, especially in text processing applications, is the one area where they are a natural fit. All their attributes complement the domain. Linear SVMs also have desirable performance characteristics.…
Stay away, in my opinion. I spent a year supporting a SVM in a production machine learning application, and it made me wish the ML research community hadn't been so in love with them for so long. They're the perfect…
Have you looked at LSM k-v stores (RocksDB being the obvious one)? Under the hood they are based on building immutable layers of data that are implicitly merged. Clones that share data are cheap (check out rocksdb-cloud…
The pitch is faster, and more space efficient since column stores are far better for analytics than row stores. Some benchmarks that found ~5-10x speedup: https://uwekorn.com/2019/10/19/taking-duckdb-for-a-spin.html…
Sorry, I should've been clearer! Beginner to ML? Stay away from SVMs. This tutorial looks good, and well written.
Agreed -- linear SVMs, especially in text processing applications, is the one area where they are a natural fit. All their attributes complement the domain. Linear SVMs also have desirable performance characteristics.…
Stay away, in my opinion. I spent a year supporting a SVM in a production machine learning application, and it made me wish the ML research community hadn't been so in love with them for so long. They're the perfect…