SQLite is easy to compile
SQLite is really easy to compile
Every blog post that took off on Hacker News, ranked by the votes of its best submission. 4,225 posts.
SQLite is really easy to compile
Benchmarks & Tips for Big Data, Hadoop, AWS, Google Cloud, PostgreSQL, Spark, Python & More...
Machine learning is part of our products, processes, and research. But computers usually don’t explain their predictions , which can cause many problems, ranging from trust issues to undetected bugs. This book is about making machine learning models and their decisions interpretable.
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Marginalia Search now properly supports phrase matching. This not only permits a more robust implementation of quoted search queries, but also helps promote results where the search terms occur in the document exactly in the same order as they do in the query. This is a write-up about implementing this change. This is…
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At RailsConf, the Citus engineering team found themselves sharing lots of useful Postgres tips on how to debug and improve the performance of your Postgres database from your Rails app. Includes topics like long-running queries, misbehaving queries, pg_stat_statements, pgBouncer, and more.
When the JPEG folks were working on image compression, they didn't create a better or faster LZ. Instead they developed new approaches. I se...
The first beta release of PostgreSQL 9.3, the latest version of the world's best open source database, is now available. …
Mixed appraisals of one of the Internet’s great friends (or foes), Wikipedia, are reflected in the slightly dystopian article “List of Wikipedia Scandals”, which sits below a Wikipedia web address. Scrutiny aside, billions of users routinely flock to the online, anonymously editable encyclopedia knowledge bank for…
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A novel data structure has been invented four times now, with three different names. We think that's a good sign.
Simplex Sigillum Veri
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Word2vec is a pervasive tool for learning word embeddings. Its success, however, is mostly due to particular architecture choices. Transferring these choices to traditional distributional methods makes them competitive with popular word embedding methods.
Every dataframe library ships with hundreds of operations. pandas alone has over 200 methods on a DataFrame. Is pivot different from melt? Is apply different from map? What about transform, agg, applymap, pipe? Some of these seem like the same operation wearing different hats. Others seem genuinely distinct. Without a…