fix typos in docs (JuliaLang#53378)

fix typos in docs

doc/src/devdocs/build/build.md

@@ -269,7 +269,7 @@ DEPS_GIT = llvm
 #  LLVM_GIT_URL = ...
 #Name of the alternate branch to clone from git
 #  LLVM_BRANCH = julia-16.0.6-0
-#SHA hash of the alterate commit to check out automatically
+#SHA hash of the alternate commit to check out automatically
 #  LLVM_SHA1 = $(LLVM_BRANCH)
 #List of LLVM targets to build.  It is strongly recommended to keep at least all the
 #default targets listed in `deps/llvm.mk`, even if you don't necessarily need all of them.

stdlib/Random/docs/src/index.md

@@ -94,7 +94,7 @@ There are two mostly orthogonal ways to extend `Random` functionalities:
 The API for 1) is quite functional, but is relatively recent so it may still have to evolve in subsequent releases of the `Random` module.
 For example, it's typically sufficient to implement one `rand` method in order to have all other usual methods work automatically.
 
-The API for 2) is still rudimentary, and may require more work than strictly necessary from the implementor,
+The API for 2) is still rudimentary, and may require more work than strictly necessary from the implementer,
 in order to support usual types of generated values.
 
 ### Generating random values of custom types
@@ -103,7 +103,7 @@ Generating random values for some distributions may involve various trade-offs.
 
 The `Random` module defines a customizable framework for obtaining random values that can address these issues. Each invocation of `rand` generates a *sampler* which can be customized with the above trade-offs in mind, by adding methods to `Sampler`, which in turn can dispatch on the random number generator, the object that characterizes the distribution, and a suggestion for the number of repetitions. Currently, for the latter, `Val{1}` (for a single sample) and `Val{Inf}` (for an arbitrary number) are used, with `Random.Repetition` an alias for both.
 
-The object returned by `Sampler` is then used to generate the random values. When implementing the random generation interface for a value `X` that can be sampled from, the implementor should define the method
+The object returned by `Sampler` is then used to generate the random values. When implementing the random generation interface for a value `X` that can be sampled from, the implementer should define the method
 
 ```julia
 rand(rng, sampler)

stdlib/Random/src/generation.jl

@@ -19,7 +19,7 @@
 Sampler(::Type{RNG}, ::Type{T}, n::Repetition) where {RNG<:AbstractRNG,T<:AbstractFloat} =
     Sampler(RNG, CloseOpen01(T), n)
 
-# generic random generation function which can be used by RNG implementors
+# generic random generation function which can be used by RNG implementers
 # it is not defined as a fallback rand method as this could create ambiguities
 
 rand(r::AbstractRNG, ::SamplerTrivial{CloseOpen01{Float16}}) =
@@ -130,7 +130,7 @@ rand(r::AbstractRNG, sp::SamplerTrivial{<:UniformBits{T}}) where {T} =
 
 #### BitInteger
 
-# rand_generic methods are intended to help RNG implementors with common operations
+# rand_generic methods are intended to help RNG implementers with common operations
 # we don't call them simply `rand` as this can easily contribute to create
 # ambiguities with user-side methods (forcing the user to resort to @eval)