A Neural Network in 11 lines of Python

This is an excellent tutorial on neural networks that does a good job of explaining not only how they work but why they work as well. The python code is very easy to follow.

There is code for a very simple example and a more advanced one.

https://iamtrask.github.io/2015/07/12/basic-python-network/

see also:

http://denson-data-science.blogspot.com/2015/09/neural-network-step-by-step.html

Neural Network: A Step by Step Backpropagation Example

This great neural network tutorial goes step-by-step through backpropagation in training a neural network. There is also companion python code.

http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/comment-page-1/

see also:

http://denson-data-science.blogspot.com/2015/09/a-neural-network-in-11-lines-of-python.html

quicksort visualization

This is a great visualization of the quicksort algorithm:

https://www.youtube.com/watch?v=aXXWXz5rF64

Quicksort (sometimes called partition-exchange sort) is an efficient sorting algorithm, serving as a systematic method for placing the elements of an array in order. Developed by Tony Hoare in 1960, it is still a very commonly used algorithm for sorting. When implemented well, it can be about two or three times faster than its main competitors, merge sort and heapsort.[1]
Quicksort is a comparison sort, meaning that it can sort items of any type for which a "less-than" relation (formally, a total order) is defined. In efficient implementations it is not a stable sort, meaning that the relative order of equal sort items is not preserved. Quicksort can operate in-place on an array, requiring small additional amounts of memory to perform the sorting.
Mathematical analysis of quicksort shows that, on average, the algorithm takes O(n log n) comparisons to sort n items. In the worst case, it makes O(n2) comparisons, though this behavior is rare.

http://en.wikipedia.org/wiki/Quicksort