Fix some dead links etc on review.

Author: Robadob

learners/acknowledgements.md

@@ -16,8 +16,8 @@ Anastasiia Shcherbakova and Mira Sarkis of [ICR-RSE](https://github.com/ICR-RSE-
 
 **Resources**
 
-Most of the content was drawn from the education and experience of the author, however the below resources provided inspiration:
+Most of the content was drawn from the education and experience of the authors, however the below resources provided inspiration:
 
-* [High Performance Python, 2nd Edition](https://www.oreilly.com/library/view/high-performance-python/9781492055013/): This excellent book goes far deeper than this short course in explaining how to maximise performance in Python, however it inspired the examples; [memory allocation is not free](optimisation-memory.html#memory-allocation-is-not-free) and [vectorisation](optimisation-memory.html#memory-allocation-is-not-free).
-* [What scientists must know about hardware to write fast code](https://viralinstruction.com/posts/hardware/): This notebook provides an array of hardware lessons relevant to programming for performance, which could be similarly found in most undergraduate Computer Science courses. Although the notebook is grounded in Julia, a lower level language than Python, it is referring to hardware so many of same lessons are covered in the [memory episode](optimisation-memory.html).
+* [High Performance Python, 2nd Edition](https://www.oreilly.com/library/view/high-performance-python/9781492055013/): This excellent book goes far deeper than this short course in explaining how to maximise performance in Python, however it inspired the examples; [memory allocation is not free](optimisation-latency.html#memory-allocation-is-not-free) and [vectorisation](optimisation-latency.html#memory-allocation-is-not-free).
+* [What scientists must know about hardware to write fast code](https://viralinstruction.com/posts/hardware/): This notebook provides an array of hardware lessons relevant to programming for performance, which could be similarly found in most undergraduate Computer Science courses. Although the notebook is grounded in Julia, a lower level language than Python, it is referring to hardware so many of same lessons are covered in the [lRWBXT episode](optimisation-latency).
 * [Why Python is Slow: Looking Under the Hood](https://jakevdp.github.io/blog/2014/05/09/why-python-is-slow/): This blog post looks under the hood of CPython to explain why Python is often slower than C (and NumPy). We reproduced two of its figures in the [optimisation introduction](optimisation-introduction.html) and [numpy](optimisation-numpy) episodes to explain how memory is laid out.

learners/technical-appendix.md

@@ -133,6 +133,9 @@ dis.dis(operatorSearch)
              54 RETURN_VALUE
 ```
 
+A naive assessment of how expensive two functions are can be carried out with this comparison.
+However this method of displaying bytecode only shows bytecode for the requested function, so it is not clear how expensive called function's will be or higher level changes to an algorithm which could reduce the number of iterations or similar.
+
 ## Hardware Level Memory Accesses
 
 The storage and movement of data plays a large role in the performance of executing software.