A notebook exploring why a sigmoid function is used in logistic regression and conditional probability HW

Got off to a good start this morning on some probability hw, showing that for a fixed $B$ with $P(B) > 0$ , that $P(\cdot | B)$ is a probability (see solution).

I also spent more time exploring the Sigmoid function resulting in this IPython notebook that attempts to explain why it is used in logistic regression. While I was at it I setup a notebooks section on the site to house the 3 notebooks so far. The sigmoid exploration was really a diversion / deep dive as part of my work on chapter 3 of the python machine learning book, which briefly covers logistic regression in its tour of classification algorithms, but it seemed like a stand alone topic that could be of interest to others and for me to come back to later.