Functions Vs. Generators
Create A Function
/* Create a function that */
def function(names):
/* For each name in a list of names */
for name in names:
/* Returns the name */
return name/* Create a variable of that function */
students = function(['Abe', 'Bob', 'Christina', 'Derek', 'Eleanor'])/* Run the function */
students'Abe'
Now we have a problem, we were only returned the name of the first student. Why? Because the function only ran the for name in names iteration once!
Create A Generator
A generator is a function, but instead of returning the return, instead returns an iterator. The generator below is exactly the same as the function above except I have replaced return with yield (which defines whether a function with a regular function or a generator function).
/* Create a generator that */
def generator(names):
/* For each name in a list of names */
for name in names:
/* Yields a generator object */
yield name/* Same as above, create a variable for the generator */
students = generator(['Abe', 'Bob', 'Christina', 'Derek', 'Eleanor'])Everything has been the same so far, but now things get interesting. Above when we ran students when it was a function, it returned one name. However, now that students refers to a generator, it yields a generator object of names!
/* Run the generator */
students<generator object generator at 0x104837a40>
What can we do this a generator object? A lot! As a generator students will can each student in the list of students:
/* Return the next student */
next(students)'Abe'
/* Return the next student */
next(students)'Bob'
/* Return the next student */
next(students)'Christina'
It is interesting to note that if we use list(students) we can see all the students still remaining in the generator object’s iteration:
/* List all remaining students in the generator */
list(students)['Derek', 'Eleanor']
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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