Haskell for Imperative Programmers #23 - Modules

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ความคิดเห็น • 8

  • @zen-ventzi-marinov
    @zen-ventzi-marinov 4 ปีที่แล้ว +4

    Great work man, really glad to see such a thorough tutorial. Hope more people join the Haskell community

  • @VladTrishch
    @VladTrishch 4 ปีที่แล้ว +5

    Thanks a lot for making this series! Could you please talk about performance profiling some time? Are there any tools commonly used for debugging memory leaks/cpu bottlenecks?

    • @philipphagenlocher
      @philipphagenlocher  4 ปีที่แล้ว +1

      Interesting! I will look into it. I don't know of any tools that are commonly in use but I do know that the compiler has some profiling options. So I will probably talk about that after I made a video on compiling binaries!

  • @Fanaro
    @Fanaro 3 ปีที่แล้ว +1

    Please do an episode on Cabal and managing your own packages.

    • @philipphagenlocher
      @philipphagenlocher  3 ปีที่แล้ว +1

      That's actually going to be the next videos topic! :)

  • @swarajhegde8107
    @swarajhegde8107 3 ปีที่แล้ว

    Can you please tell me how to solve this:
    Define a module Example.ExpressionTree
    ExpressionTree should be of kind * -> * meaning that its type of value is not fixed.
    An instance of ExpressionTree should be either a simple value or an operation node.
    An operation node has a function defining the operation, a string describing the function
    and two subtrees, each of which are ExpressionTrees.
    Define (and export) an evaluate function that will evaluate an entire ExpressionTree,
    yielding the result.
    So evaluate $ OpNode (+) "+" (ValueNode 3) (ValueNode 4)
    should return 7
    Finally, make ExpressionTree an instance of Show such that it displays the tree in pre-
    order. So
    show $ OpNode (*) "*" (ValueNode 2) $ OpNode (+) "+" (ValueNode 3) (ValueNode
    4)
    should return "* 2 + 3 4"

  • @ravikumar-vr6zm
    @ravikumar-vr6zm 4 ปีที่แล้ว

    Make video on Haskell for data science ........I 'm waiting I really interested to learn functional paradigm for Machine learnig and AI . I heard that parallel computing easy in functional programming.
    two demand
    1. AI with haskell
    2. parallel computing in haskell

    • @philipphagenlocher
      @philipphagenlocher  4 ปีที่แล้ว +1

      Parallelism will be a future topic!
      When it comes to machine learning it depends on what kind of algorithms you want to implement for the specific learning task. State of the art ML frameworks like Tensorflow have Haskell bindings ( github.com/tensorflow/haskell ) so they work out of the box. ML in itself is not really specific to the language but a broader topic outside of the programming language used.