[julia-users] Re: Good, short set of slides introducing Julia

[andrew cooke]

oops, yes thanks.

On Thursday, 10 September 2015 08:42:01 UTC-3, Jeffrey Sarnoff wrote:
>
> if you want to get into it, 
>
> immutable Complex ... end
>
> does what type Complex ... end does with the added benefit of behaving 
> more as Float64 does with respect to memory and access at the price of not 
> being able to change its component values 
>
> and none of that is needed, though it does make a good example, because 
> Complex is already available:
>
> julia> c = Complex(1.5,0.5)
> 1.5 + 0.5im
>
> julia> typeof(c)
> Complex{Float64}
>
>
> On Thursday, September 10, 2015 at 7:29:50 AM UTC-4, Jeffrey Sarnoff wrote:
>>
>>   type Complex{ T<:AbstractFloat } <: Number
>>   x::T
>>   y::T
>>   end
>>
>>
>> On Thursday, September 10, 2015 at 7:18:13 AM UTC-4, andrew cooke wrote:
>>>
>>> thanks (how does someone working on embedded c++ get to work with 
>>> adaboost?!)
>>>
>>> what i am actually going with is a bunch of links to examples (i can 
>>> email this to everyone before the talk, which will be over google meetup):
>>>
>>> (if anyone has corrections to what follows in the next few hours i am 
>>> happy to hear them, although i think it's pretty uncontroversial)
>>>
>>>
>>> * Data analysis and plots something like R
>>>   http://dcjones.github.io/Gadfly.jl/
>>>
>>> * IJulia reminds me of Mathematica (based on IPython)
>>>   
>>> https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#julia
>>>   
>>> http://nbviewer.ipython.org/url/beowulf.csail.mit.edu/18.337/fractals.ipynb
>>>   
>>> http://nbviewer.ipython.org/github/JuliaOpt/juliaopt-notebooks/blob/master/notebooks/Matrix%20Completion%20with%20Binary%20Data.ipynb
>>>
>>> * Like Matlab, it makes using arrays easy
>>>   http://quant-econ.net/jl/julia_arrays.html
>>>
>>> * The neat thing (to me) is that unlike Matlab, the arrays code is often
>>>   (not always - it calls out to BLAS etc) written in Julia itself.
>>>   http://julialang.org/benchmarks/
>>>
>>> * More generally, it has managed ("automatic") memory allocation.
>>>   It doesn't have objects/classes, but it has something similar:
>>>   it combines types (like C structs) with multiple dispatch.
>>>
>>>   For example, to define complex types:
>>>
>>>   type Complex64<:Number
>>>   Float64 x
>>>   Float64 y
>>>   end
>>>
>>>   and then define
>>>
>>>   +(a::Complex64, b::Complex64) = Complex64(a.x + b.x, a.y + b.y)
>>>   ... etc
>>>
>>>   Note that + is already defined for a pile of other types (start julia 
>>> and
>>>   type "methods(+)" to see them all).
>>>
>>>   Which is almost the same as defining a Complex64 class with a "+" 
>>> method.
>>>   Main differences are (1) all types used to choose method and (2) only 
>>> final
>>>   types have fields (so memory layout is known).
>>>
>>>   And it's fast because the compiler compiles functions at runtime 
>>> depending
>>>   on types when called.  So the code ends up being compiled for a very
>>>   specific type, even if in your code you just had "a+b" and it wasn't 
>>> clear
>>>   whether a and b were Complex64 or Float64 or Int32 or ...
>>>
>>>   Downside to that is that when you first run a program it is actually 
>>> slow,
>>>   as it compiles things.  But second and further calls to any routine are
>>>   fast.
>>>
>>>   I've written CRC32 (checksum) code of comparable speed to libz (pretty 
>>> much
>>>   the C benchmark).  It wasn't "simple", but it was no harder than C.  
>>> You
>>>   have profiling tools, you unroll loops, etc etc.
>>>
>>>   (In practice you would probably do:
>>>
>>>   type Complex(F<:Float}<:Number
>>>   F x
>>>   F y
>>>   end
>>>
>>>   because it hs parameterised types)
>>>
>>>
>>> On Thursday, 10 September 2015 05:21:42 UTC-3, Carlos Becker wrote:

 Hi Andrew, my slides are here, 
 https://sites.google.com/site/carlosbecker/a-few-notes , they are for 
 v0.3: 

 If you need the openoffice original let me know, I can send it to you.
 Cheers.

 El miércoles, 9 de septiembre de 2015, 14:07:36 (UTC+2), andrew cooke 
 escribió:
>
> ok, thanks everyone i'll have a look at all those.  andrew
>
> On Tuesday, 8 September 2015 17:58:33 UTC-3, andrew cooke wrote:
>>
>>
>> I need to give a presentation at work and was wondering is slides 
>> already exist that:
>>
>>   * show how fast it is in benchmarks
>>
>>   * show that it's similar to matlab (matrix stuff)
>>
>>   * show that you can write fast inner loops
>>
>>  For bonus points:
>>
>>   * show how you can add other numerical types at no "cost"
>>
>>   * show how mutiple dispatch can be useful
>>
>>   * show how someone used to OO in, say, python, won't feel too lost
>>
>> Preferably just one slide per point.  Very short.
>>
>> Thanks,
>> Andrew
>>
>>

[julia-users] Re: Good, short set of slides introducing Julia

[Jeffrey Sarnoff]

if you want to get into it, 

immutable Complex ... end

does what type Complex ... end does with the added benefit of behaving more 
as Float64 does with respect to memory and access at the price of not being 
able to change its component values 

and none of that is needed, though it does make a good example, because 
Complex is already available:

julia> c = Complex(1.5,0.5)
1.5 + 0.5im

julia> typeof(c)
Complex{Float64}


On Thursday, September 10, 2015 at 7:29:50 AM UTC-4, Jeffrey Sarnoff wrote:
>
>   type Complex{ T<:AbstractFloat } <: Number
>   x::T
>   y::T
>   end
>
>
> On Thursday, September 10, 2015 at 7:18:13 AM UTC-4, andrew cooke wrote:
>>
>> thanks (how does someone working on embedded c++ get to work with 
>> adaboost?!)
>>
>> what i am actually going with is a bunch of links to examples (i can 
>> email this to everyone before the talk, which will be over google meetup):
>>
>> (if anyone has corrections to what follows in the next few hours i am 
>> happy to hear them, although i think it's pretty uncontroversial)
>>
>>
>> * Data analysis and plots something like R
>>   http://dcjones.github.io/Gadfly.jl/
>>
>> * IJulia reminds me of Mathematica (based on IPython)
>>   
>> https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#julia
>>   
>> http://nbviewer.ipython.org/url/beowulf.csail.mit.edu/18.337/fractals.ipynb
>>   
>> http://nbviewer.ipython.org/github/JuliaOpt/juliaopt-notebooks/blob/master/notebooks/Matrix%20Completion%20with%20Binary%20Data.ipynb
>>
>> * Like Matlab, it makes using arrays easy
>>   http://quant-econ.net/jl/julia_arrays.html
>>
>> * The neat thing (to me) is that unlike Matlab, the arrays code is often
>>   (not always - it calls out to BLAS etc) written in Julia itself.
>>   http://julialang.org/benchmarks/
>>
>> * More generally, it has managed ("automatic") memory allocation.
>>   It doesn't have objects/classes, but it has something similar:
>>   it combines types (like C structs) with multiple dispatch.
>>
>>   For example, to define complex types:
>>
>>   type Complex64<:Number
>>   Float64 x
>>   Float64 y
>>   end
>>
>>   and then define
>>
>>   +(a::Complex64, b::Complex64) = Complex64(a.x + b.x, a.y + b.y)
>>   ... etc
>>
>>   Note that + is already defined for a pile of other types (start julia 
>> and
>>   type "methods(+)" to see them all).
>>
>>   Which is almost the same as defining a Complex64 class with a "+" 
>> method.
>>   Main differences are (1) all types used to choose method and (2) only 
>> final
>>   types have fields (so memory layout is known).
>>
>>   And it's fast because the compiler compiles functions at runtime 
>> depending
>>   on types when called.  So the code ends up being compiled for a very
>>   specific type, even if in your code you just had "a+b" and it wasn't 
>> clear
>>   whether a and b were Complex64 or Float64 or Int32 or ...
>>
>>   Downside to that is that when you first run a program it is actually 
>> slow,
>>   as it compiles things.  But second and further calls to any routine are
>>   fast.
>>
>>   I've written CRC32 (checksum) code of comparable speed to libz (pretty 
>> much
>>   the C benchmark).  It wasn't "simple", but it was no harder than C.  You
>>   have profiling tools, you unroll loops, etc etc.
>>
>>   (In practice you would probably do:
>>
>>   type Complex(F<:Float}<:Number
>>   F x
>>   F y
>>   end
>>
>>   because it hs parameterised types)
>>
>>
>> On Thursday, 10 September 2015 05:21:42 UTC-3, Carlos Becker wrote:
>>>
>>> Hi Andrew, my slides are here, 
>>> https://sites.google.com/site/carlosbecker/a-few-notes , they are for 
>>> v0.3: 
>>>
>>> If you need the openoffice original let me know, I can send it to you.
>>> Cheers.
>>>
>>> El miércoles, 9 de septiembre de 2015, 14:07:36 (UTC+2), andrew cooke 
>>> escribió:

 ok, thanks everyone i'll have a look at all those.  andrew

 On Tuesday, 8 September 2015 17:58:33 UTC-3, andrew cooke wrote:
>
>
> I need to give a presentation at work and was wondering is slides 
> already exist that:
>
>   * show how fast it is in benchmarks
>
>   * show that it's similar to matlab (matrix stuff)
>
>   * show that you can write fast inner loops
>
>  For bonus points:
>
>   * show how you can add other numerical types at no "cost"
>
>   * show how mutiple dispatch can be useful
>
>   * show how someone used to OO in, say, python, won't feel too lost
>
> Preferably just one slide per point.  Very short.
>
> Thanks,
> Andrew
>
>

[julia-users] Re: Good, short set of slides introducing Julia

[Jeffrey Sarnoff]

  type Complex{ T<:AbstractFloat } <: Number
  x::T
  y::T
  end


On Thursday, September 10, 2015 at 7:18:13 AM UTC-4, andrew cooke wrote:
>
> thanks (how does someone working on embedded c++ get to work with 
> adaboost?!)
>
> what i am actually going with is a bunch of links to examples (i can email 
> this to everyone before the talk, which will be over google meetup):
>
> (if anyone has corrections to what follows in the next few hours i am 
> happy to hear them, although i think it's pretty uncontroversial)
>
>
> * Data analysis and plots something like R
>   http://dcjones.github.io/Gadfly.jl/
>
> * IJulia reminds me of Mathematica (based on IPython)
>   
> https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#julia
>   
> http://nbviewer.ipython.org/url/beowulf.csail.mit.edu/18.337/fractals.ipynb
>   
> http://nbviewer.ipython.org/github/JuliaOpt/juliaopt-notebooks/blob/master/notebooks/Matrix%20Completion%20with%20Binary%20Data.ipynb
>
> * Like Matlab, it makes using arrays easy
>   http://quant-econ.net/jl/julia_arrays.html
>
> * The neat thing (to me) is that unlike Matlab, the arrays code is often
>   (not always - it calls out to BLAS etc) written in Julia itself.
>   http://julialang.org/benchmarks/
>
> * More generally, it has managed ("automatic") memory allocation.
>   It doesn't have objects/classes, but it has something similar:
>   it combines types (like C structs) with multiple dispatch.
>
>   For example, to define complex types:
>
>   type Complex64<:Number
>   Float64 x
>   Float64 y
>   end
>
>   and then define
>
>   +(a::Complex64, b::Complex64) = Complex64(a.x + b.x, a.y + b.y)
>   ... etc
>
>   Note that + is already defined for a pile of other types (start julia and
>   type "methods(+)" to see them all).
>
>   Which is almost the same as defining a Complex64 class with a "+" method.
>   Main differences are (1) all types used to choose method and (2) only 
> final
>   types have fields (so memory layout is known).
>
>   And it's fast because the compiler compiles functions at runtime 
> depending
>   on types when called.  So the code ends up being compiled for a very
>   specific type, even if in your code you just had "a+b" and it wasn't 
> clear
>   whether a and b were Complex64 or Float64 or Int32 or ...
>
>   Downside to that is that when you first run a program it is actually 
> slow,
>   as it compiles things.  But second and further calls to any routine are
>   fast.
>
>   I've written CRC32 (checksum) code of comparable speed to libz (pretty 
> much
>   the C benchmark).  It wasn't "simple", but it was no harder than C.  You
>   have profiling tools, you unroll loops, etc etc.
>
>   (In practice you would probably do:
>
>   type Complex(F<:Float}<:Number
>   F x
>   F y
>   end
>
>   because it hs parameterised types)
>
>
> On Thursday, 10 September 2015 05:21:42 UTC-3, Carlos Becker wrote:
>>
>> Hi Andrew, my slides are here, 
>> https://sites.google.com/site/carlosbecker/a-few-notes , they are for 
>> v0.3: 
>>
>> If you need the openoffice original let me know, I can send it to you.
>> Cheers.
>>
>> El miércoles, 9 de septiembre de 2015, 14:07:36 (UTC+2), andrew cooke 
>> escribió:
>>>
>>> ok, thanks everyone i'll have a look at all those.  andrew
>>>
>>> On Tuesday, 8 September 2015 17:58:33 UTC-3, andrew cooke wrote:


 I need to give a presentation at work and was wondering is slides 
 already exist that:

   * show how fast it is in benchmarks

   * show that it's similar to matlab (matrix stuff)

   * show that you can write fast inner loops

  For bonus points:

   * show how you can add other numerical types at no "cost"

   * show how mutiple dispatch can be useful

   * show how someone used to OO in, say, python, won't feel too lost

 Preferably just one slide per point.  Very short.

 Thanks,
 Andrew

[julia-users] Re: Good, short set of slides introducing Julia

[andrew cooke]

thanks (how does someone working on embedded c++ get to work with 
adaboost?!)

what i am actually going with is a bunch of links to examples (i can email 
this to everyone before the talk, which will be over google meetup):

(if anyone has corrections to what follows in the next few hours i am happy 
to hear them, although i think it's pretty uncontroversial)


* Data analysis and plots something like R
  http://dcjones.github.io/Gadfly.jl/

* IJulia reminds me of Mathematica (based on IPython)
  
https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#julia
  
http://nbviewer.ipython.org/url/beowulf.csail.mit.edu/18.337/fractals.ipynb
  
http://nbviewer.ipython.org/github/JuliaOpt/juliaopt-notebooks/blob/master/notebooks/Matrix%20Completion%20with%20Binary%20Data.ipynb

* Like Matlab, it makes using arrays easy
  http://quant-econ.net/jl/julia_arrays.html

* The neat thing (to me) is that unlike Matlab, the arrays code is often
  (not always - it calls out to BLAS etc) written in Julia itself.
  http://julialang.org/benchmarks/

* More generally, it has managed ("automatic") memory allocation.
  It doesn't have objects/classes, but it has something similar:
  it combines types (like C structs) with multiple dispatch.

  For example, to define complex types:

  type Complex64<:Number
  Float64 x
  Float64 y
  end

  and then define

  +(a::Complex64, b::Complex64) = Complex64(a.x + b.x, a.y + b.y)
  ... etc

  Note that + is already defined for a pile of other types (start julia and
  type "methods(+)" to see them all).

  Which is almost the same as defining a Complex64 class with a "+" method.
  Main differences are (1) all types used to choose method and (2) only 
final
  types have fields (so memory layout is known).

  And it's fast because the compiler compiles functions at runtime depending
  on types when called.  So the code ends up being compiled for a very
  specific type, even if in your code you just had "a+b" and it wasn't clear
  whether a and b were Complex64 or Float64 or Int32 or ...

  Downside to that is that when you first run a program it is actually slow,
  as it compiles things.  But second and further calls to any routine are
  fast.

  I've written CRC32 (checksum) code of comparable speed to libz (pretty 
much
  the C benchmark).  It wasn't "simple", but it was no harder than C.  You
  have profiling tools, you unroll loops, etc etc.

  (In practice you would probably do:

  type Complex(F<:Float}<:Number
  F x
  F y
  end

  because it hs parameterised types)


On Thursday, 10 September 2015 05:21:42 UTC-3, Carlos Becker wrote:
>
> Hi Andrew, my slides are here, 
> https://sites.google.com/site/carlosbecker/a-few-notes , they are for 
> v0.3: 
>
> If you need the openoffice original let me know, I can send it to you.
> Cheers.
>
> El miércoles, 9 de septiembre de 2015, 14:07:36 (UTC+2), andrew cooke 
> escribió:
>>
>> ok, thanks everyone i'll have a look at all those.  andrew
>>
>> On Tuesday, 8 September 2015 17:58:33 UTC-3, andrew cooke wrote:
>>>
>>>
>>> I need to give a presentation at work and was wondering is slides 
>>> already exist that:
>>>
>>>   * show how fast it is in benchmarks
>>>
>>>   * show that it's similar to matlab (matrix stuff)
>>>
>>>   * show that you can write fast inner loops
>>>
>>>  For bonus points:
>>>
>>>   * show how you can add other numerical types at no "cost"
>>>
>>>   * show how mutiple dispatch can be useful
>>>
>>>   * show how someone used to OO in, say, python, won't feel too lost
>>>
>>> Preferably just one slide per point.  Very short.
>>>
>>> Thanks,
>>> Andrew
>>>
>>>

[julia-users] Re: Good, short set of slides introducing Julia

[Carlos Becker]

Hi Andrew, my slides are here, 
https://sites.google.com/site/carlosbecker/a-few-notes , they are for v0.3: 

If you need the openoffice original let me know, I can send it to you.
Cheers.

El miércoles, 9 de septiembre de 2015, 14:07:36 (UTC+2), andrew cooke 
escribió:
>
> ok, thanks everyone i'll have a look at all those.  andrew
>
> On Tuesday, 8 September 2015 17:58:33 UTC-3, andrew cooke wrote:
>>
>>
>> I need to give a presentation at work and was wondering is slides already 
>> exist that:
>>
>>   * show how fast it is in benchmarks
>>
>>   * show that it's similar to matlab (matrix stuff)
>>
>>   * show that you can write fast inner loops
>>
>>  For bonus points:
>>
>>   * show how you can add other numerical types at no "cost"
>>
>>   * show how mutiple dispatch can be useful
>>
>>   * show how someone used to OO in, say, python, won't feel too lost
>>
>> Preferably just one slide per point.  Very short.
>>
>> Thanks,
>> Andrew
>>
>>

[julia-users] Re: Good, short set of slides introducing Julia

[andrew cooke]

ok, thanks everyone i'll have a look at all those.  andrew

On Tuesday, 8 September 2015 17:58:33 UTC-3, andrew cooke wrote:
>
>
> I need to give a presentation at work and was wondering is slides already 
> exist that:
>
>   * show how fast it is in benchmarks
>
>   * show that it's similar to matlab (matrix stuff)
>
>   * show that you can write fast inner loops
>
>  For bonus points:
>
>   * show how you can add other numerical types at no "cost"
>
>   * show how mutiple dispatch can be useful
>
>   * show how someone used to OO in, say, python, won't feel too lost
>
> Preferably just one slide per point.  Very short.
>
> Thanks,
> Andrew
>
>

[julia-users] Re: Good, short set of slides introducing Julia

[cormullion]

google for Carlos Becker's Julia presentation -- nice looking short 
presentation comparing Julia with Matlab.

[julia-users] Re: Good, short set of slides introducing Julia

[cdm]

maybe helpful, maybe not ...

some links from the main Julia page:

   http://nbviewer.ipython.org/url/julialang.org/benchmarks.ipynb

   http://julialang.org/benchmarks.csv



obviously, the most "famous" collection
of the benchmark data from the
"High-Performance JIT Compiler"

section of the main Julia page

   http://julialang.org/

has seen plenty of slide time ...



this deck is a bit dated, but maybe fine:

   http://www.slideshare.net/OReillyStrata/strata-santa-clara-february-2013


good luck !!!

[julia-users] Re: Good, short set of slides introducing Julia

[Jeffrey Sarnoff]

I had thought they might have some specific slides that would be helpful. 
 In my experience, whatever the "common task," the audience and the 
intended result shape the contents of (some of) the presentation.  A quick 
overview of major ways that Julia advances the both the efficacy and the 
art of scientific programming is a presentation others will want to see.  
You may have looked at http://julialang.org/teaching/ and 
http://julialang.org/learning/ (tutorials) 00 it  is worth a second look 
with an eye toward what might make a good slide or two.
For your purpose these two pages are important performance-tips 
 and 
fast-numerics . There may 
be something already done that would work for you -- or not (dunno).  If 
you are comfortable with some manner of producing slides, give it a go.  I 
have found that organizing and integrating mulitsourced information leads 
to a more authoritative presentation.

If you go forward and want some feedback on the way -- let me know.



On Tuesday, September 8, 2015 at 5:28:04 PM UTC-4, andrew cooke wrote:
>
> i'd actually already looked at those (should have said, sorry) - they're 
> longer than what i was needing, but i could pick a few slides.  i just 
> wondered if what i was doing (basically, selling to scientific programmers 
> in < 10 mins) was a common task.
>
> andrew
>
> On Tuesday, 8 September 2015 18:18:25 UTC-3, Jeffrey Sarnoff wrote:
>>
>> Here is one place to look svaksha's list of slide presentations 
>> .
>>
>> For those who may not be familiar with details of copyright 
>> (a) written material is copyrighted even when there is no formal 
>> copyright statement
>> (b) it is the responsibility of the person who wants to use the 
>> material to get the author's permission
>> (sometimes that permission may accompany the material, e.g. 
>> "this material is placed in the public domain", or with some other 
>> permissive language)
>> (c) when using another person's written work, cite the person and 
>> identify the work so others can find it if they want
>>
>> The Julia community is more helpful and supportive than most; so, go 
>> ahead and ask, and let the person know you would cite their work.
>>
>>
>> On Tuesday, September 8, 2015 at 4:58:33 PM UTC-4, andrew cooke wrote:
>>>
>>>
>>> I need to give a presentation at work and was wondering is slides 
>>> already exist that:
>>>
>>>   * show how fast it is in benchmarks
>>>
>>>   * show that it's similar to matlab (matrix stuff)
>>>
>>>   * show that you can write fast inner loops
>>>
>>>  For bonus points:
>>>
>>>   * show how you can add other numerical types at no "cost"
>>>
>>>   * show how mutiple dispatch can be useful
>>>
>>>   * show how someone used to OO in, say, python, won't feel too lost
>>>
>>> Preferably just one slide per point.  Very short.
>>>
>>> Thanks,
>>> Andrew
>>>
>>>

[julia-users] Re: Good, short set of slides introducing Julia

[andrew cooke]

i'd actually already looked at those (should have said, sorry) - they're 
longer than what i was needing, but i could pick a few slides.  i just 
wondered if what i was doing (basically, selling to scientific programmers 
in < 10 mins) was a common task.

andrew

On Tuesday, 8 September 2015 18:18:25 UTC-3, Jeffrey Sarnoff wrote:
>
> Here is one place to look svaksha's list of slide presentations 
> .
>
> For those who may not be familiar with details of copyright 
> (a) written material is copyrighted even when there is no formal 
> copyright statement
> (b) it is the responsibility of the person who wants to use the 
> material to get the author's permission
> (sometimes that permission may accompany the material, e.g. 
> "this material is placed in the public domain", or with some other 
> permissive language)
> (c) when using another person's written work, cite the person and 
> identify the work so others can find it if they want
>
> The Julia community is more helpful and supportive than most; so, go ahead 
> and ask, and let the person know you would cite their work.
>
>
> On Tuesday, September 8, 2015 at 4:58:33 PM UTC-4, andrew cooke wrote:
>>
>>
>> I need to give a presentation at work and was wondering is slides already 
>> exist that:
>>
>>   * show how fast it is in benchmarks
>>
>>   * show that it's similar to matlab (matrix stuff)
>>
>>   * show that you can write fast inner loops
>>
>>  For bonus points:
>>
>>   * show how you can add other numerical types at no "cost"
>>
>>   * show how mutiple dispatch can be useful
>>
>>   * show how someone used to OO in, say, python, won't feel too lost
>>
>> Preferably just one slide per point.  Very short.
>>
>> Thanks,
>> Andrew
>>
>>

[julia-users] Re: Good, short set of slides introducing Julia

[Jeffrey Sarnoff]

Here is one place to look svaksha's list of slide presentations 
.

For those who may not be familiar with details of copyright 
(a) written material is copyrighted even when there is no formal 
copyright statement
(b) it is the responsibility of the person who wants to use the 
material to get the author's permission
(sometimes that permission may accompany the material, e.g. 
"this material is placed in the public domain", or with some other 
permissive language)
(c) when using another person's written work, cite the person and 
identify the work so others can find it if they want

The Julia community is more helpful and supportive than most; so, go ahead 
and ask, and let the person know you would cite their work.


On Tuesday, September 8, 2015 at 4:58:33 PM UTC-4, andrew cooke wrote:
>
>
> I need to give a presentation at work and was wondering is slides already 
> exist that:
>
>   * show how fast it is in benchmarks
>
>   * show that it's similar to matlab (matrix stuff)
>
>   * show that you can write fast inner loops
>
>  For bonus points:
>
>   * show how you can add other numerical types at no "cost"
>
>   * show how mutiple dispatch can be useful
>
>   * show how someone used to OO in, say, python, won't feel too lost
>
> Preferably just one slide per point.  Very short.
>
> Thanks,
> Andrew
>
>