My point is that contemporary Data Science stack is using too many
different languages all way from scripting (R, Python) to statically
compiled C/C++ and sometimes Fortran (R, some scipy algos are in Fortran)
and even JVM based Scala. This creates artificial barriers -- data
scientists play th
Hi Michael,
thanks for your reply. The current problem with Data Science ecosystem
(from Data Analysis all way to GPU based ML) is that it employs a whole
stack of languages from low-level like C (and sometimes assembler) all way
to scripting like Python or R. In parallel, there are Big Data too
On Tue, Jul 16, 2019 at 7:18 PM Slonik Az wrote:
> REPL in a static AOT compiled language is hard, yet Swift somehow managed
> to implement it.
>
>
I must disagree. The technique is somewhat well known and has a long
history. See e.g., various common lisp, and standard ml implementations. If
you
Leo,
R is implemented in C and FORTRAN plus R on top of that. SAS is in C (and
some Go here and there) plus the SAS language in top of that. Mathematica
is implemented in C/C++ with the "Wolfram Language" on top of that. PARI/GP
is implemented in C plus some GP-language code. Macsyma, Maple, Octav
Hi,
I would start with:
- gonum.org
- gopherdata.io
I have also started a little series about how to apply Go and Gonum to
stats (in high energy physics but that's just the setup)
sbinet.github.io
/shameless-plug off.
hth,
-s
sent from my droid
On Nov 6, 2017 10:46 AM, "Vikram Rawat" wrote
