I just had a quick look. Here are some ideas for a few exercices.
You can use list comprehension in some exercices e.g.
Checkerboard pattern
Float64[(i+j)%2 for i=1:8, j=1:8]
10x10 matrix with row values ranging from 0 to 9
Float64[j for i=0:9, j=0:9 ]
It seems that what is called a scalar type in numpy is a subtype of the
Signed abstract type in Julia.
Print the minimum and maximum representable value for each Julia scalar
type
print(map!(t -> (typemin(t),typemax(t)), subtypes(Signed)))
Generators can be built with closures. In what follows, I am generating the
multiples of 2 in an array.
Consider a generator function that generates 10 integers and use it to
build an array
makegen(n) = let r = 0 ; () -> (r+=n ; r) ; end ; mygen = makegen(2) ;
[mygen() for i = 1:10]
Best,
