deep-diamond VS tech.ml.dataset

Do clojure as a side gig or in free time. Let day job pay the bills. If you can, maybe incorporate clojure into work job to solve small problems (https://github.com/clj-python/libpython-clj and https://github.com/scicloj/clojisr provide bridges to/from python and r). There is a lot of effort going into the data science side as well; the scicloj effort has resulted in a lot of growth over the last 2 years. tech.ml.dataset, tech.ml (now scicloj.ml). Dragan has a bunch of excellent stuff in neanderthal and deep diamond. There are also bindings to other jvm libraries from multiple languages.

Really high quality libraries for deep learning, dataset manipulation, and more

tech.ml.dataset has seen some major upgrades for discoverability - specifically the tech.ml.dataset main namespace has been revamped. If you use Cursive or Calva your intellisense will now work with the main namespaces.

It is based on a state-of-the art , high performance tabular dataset implementation, tech.ml.dataset and combines it with a innovative pipeline approach build with idiomatic and functional Clojure concepts in mind. Machine Learning models get pulled in as plugins from existing ecosystems, so are available from the start. Please find user guides and example code in GitHub at https://github.com/scicloj/scicloj.ml

Chris Nuernberger (https://github.com/cnuernber/) will be presenting a talk about his work with dtype-next (https://github.com/cnuernber/dtype-next) and tech.ml.dataset (https://github.com/techascent/tech.ml.dataset) the new high performance data processing system for Clojure. His talk will cover the axiomatic basis of array programming, some interesting high performance primitives, and how representing your problem with a column-major map-of-arrays design gives you some interesting properties as compared to your standard Clojure row-major sequence-of-maps design.

Announcing tech.ml.dataset to Reddit :-). tmd is a data processing/dataframe system in the same vein as Pandas and R's dplyr or data table.

I appreciate the feedback. Fixed

For single-node work for ETL stuff, tech.ml.dataset is the emerging standard and is very efficient and capable of interop with various storage medium (including arrow, parquet, etc.). It has the ability to work with larger-than-memory data as well, although currently not use in a distributed fashion, so single-machine only. tablecloth is a dyplr-familiar clojure API on top of tech.ml.dataset.