are accessible with common knowledge to someone with programming experience but without a background in mathematics?
def r(some_params):
if predicate_11(some_params): return s(f1(some_params))
else: return s(f2(some_params))
def s(some_params):
if predicate_21(some_params): return r(some_params)
else: return t(f3(some_params))
def t(some_params):
if predicate_31(some_params): return f4(some_params)
elseif predicate_23(some_params): return r(some_params)
else: return t(f3(some_params))
The second one, structurally, can also be understood without knowing math but what's actually executed does require some familiarity with math. It's, like above, using a conditional expression described earlier and the lambda notation for defining anonymous functions (to be clear, he also uses it as an example of something that's not quite valid since the name `sqrt` will not be bound inside the lambda, but we can approximate it, invalid multi-line Python lambda incoming):
sqrt = lambda a, x, epsilon: if abs(x*x - a) < epsilon: return x # that is, we've found a close-enough approximation
else: return sqrt(a, (x+a/x)/2, epsilon) # get a closer approximation to the square root
def sqrt(a,x,epsilon):
if abs(x*x-a) < epsilon: return x
else: return sqrt(a, (x+a/x)/2, epsilon)
Both of those are there to motivate the introduction of the label form at the bottom of that section.