This blog is part of FSAdvent F# calendar 2019. Jupyter has been around for ages. The data scientist’s one of the favorite tools. Kind of best thing to write words, equations and result in the best possible manner. F# is and was a poster child for DotNet to do Data Science. I never liked the poster child part but still, it is what it is. I ’ll come to that little later on.
I am writing after a long time for F#. And this would be a little bit controversial post. Take it with a pinch of salt. Recently I got a call from some company who wants me to do some job in F#, for its client. The client took my interview and told that its client wants to do some work in F#. Things were going smoothly until the interviewer asked me that I should be writing F# code following standard and design patterns.
Happy New Year I like to start with wishing all the readers A Very Happy New Year… I wish 2019 for you will be too much fun and less of issues. Speaking of issues, will going to talk about Functional Programming. Because if you are coding with Functional Programming you normally get fewer errors. I love them for those reasons. In this post also as the title suggests I will be going to talk about Functional Programming only.
I don’t know I should be writing this or not. But I am giving it a go. This article is part of FsAdvent calender. I wanted to use Tensorflow JS with Fable and specifically Fable - Elmish application. But I failed to do so. There are a couple of reasons I have found out. Reader may solve the issue or point out other issues. I am all ears. Let me explain the setup, I don’t want any extra elements in the mix, so I started with bare minimum set up.
Everyone knows about the SAFE Stack, if you don’t please do check it out - it’s a great way to do full-stack web development in F#, and may be the “coolest” thing about .net these days. If you go to the documentations, there are couple of deployment options. It works like a charm especially with docker. You can deploy the output docker image to a public / private docker registry, or create a private Azure registry and store it there.
Few days back I did a poll that which new shiny library I should be trying. Here is result of that I am thinking of trying one of the #MachineLearning- #DeepLearninglibrary. Which one I should be trying? It should be fun and if possible can be used with #fsharpand / or #dotnetcore. I ll share my experience in blog / video. Please RT.— Kunjan Dalal (@kunjee) October 18, 2018As per popular demand I should be trying ML.
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