• Liked Pushkar Kulkarni
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    Going Swiftly Functional

    Pushkar Kulkarni
    Pushkar Kulkarni
    Mamatha Busi
    Mamatha Busi
    schedule 7 months ago
    Sold Out!
    90 mins
    Tutorial
    Beginner

    Two years after its introduction, the Swift programming language finds itself at rank # 14 on the TIOBE index for July 2016. Some of the prime reasons for Swift’s meteoric rise are its carefully designed syntax and semantics - it borrows heavily from the best features of a wide range of languages. The syntax is intuitive and new Swift learners can often relate it to the language of their choice. Introduced primarily as a client-side language, Swift was open sourced in December 2015 via swift.org. Since then, there has been a community effort on extending its capabilities to the server side too. Swift is available on Linux today, and it is poised to become a language enabling end to end development - from mobile apps to server backends. 

    Swift falls in the category of “modern native languages” - like Rust and Go. These runtimes run code in bare metal, boosting performance. They are statically, strongly typed and with type-inference. This makes them safe. Their modern syntax also makes them expressive enough for high level abstractions like those seen in functional programming. They try to solve some of the tradeoffs that languages of the past battled with. On the other hand, functional programming has found new oxygen in the last couple of years when agile methodologies become as pervasive as multicore hardware . While we do have languages like Haskell that are purely functional and provide excellent support to express functional concepts, it is important to separate functional thinking away from the language. Functional programming is supported by most of the languages today because the returns of it in terms of expression, conciseness, readability, correctness and performance are unmatched. Swift is not a purely functional language. However, it provides syntactic and semantic support for functional programming to a large extent.

  • Liked Viral B. Shah
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    Julia - A Lisp for Fast Number Crunching

    Viral B. Shah
    Viral B. Shah
    Shashi Gowda
    Shashi Gowda
    schedule 6 months ago
    Sold Out!
    90 mins
    Workshop
    Intermediate

    Julia is a programming language for data science and numerical computing. Julia is a high-level, high-performance dynamic language, it uses just-in-time compilation to provide fast machine code - dynamic code runs at about half the speed of C, and orders of magnitude faster than traditional numerical computing tools.

    Julia borrows two main ideas from Lisp lore:

    1. Multiple-dispatch: a method is identified by a name and the types of all of its arguments (traditional OOP only uses the type of a single argument) multiple-dispatch allows for a natural programming model suitable for mathematics and general purpose programming. Interestingly, the same feature is responsible for Julia's ability to generate really fast machine code.
    2. Macros: Julia has syntax that is familiar to users of other technical computing environments, however, Julia expressions are homoiconic -- Julia code can be represented as Julia data structures, and transformed from one form to another via hygienic macros. There are some very interesting uses of macros to create domain-specific languages for effective programming. 
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