The Sigmoid Function in Logistic Regression | iPython Notebooks
The Sigmoid Function in Logistic Regression¶In learning about logistic regression, I was at first confused as to why a sigmoid function was used to map from the inputs to the predicted output. I mean, sure, it's a nice function that cleanly maps from any real number to a range of $-1$ to $1$, but where did it come from? This notebook hopes to explain. Logistic Regression¶With classification, we ha
A Concrete Introduction to Probability (using Python)
A Concrete Introduction to Probability (using Python)¶This notebook covers the basics of probability theory, with Python 3 implementations. (You should have some background in probability and Python.) In 1814, Pierre-Simon Laplace wrote: Probability ... is thus simply a fraction whose numerator is the number of favorable cases and whose denominator is the number of all the cases possible ... when
Gradient Descent with Backpropagation – Δ ℚuantitative √ourney – ∑ My experiences in learning quantitative applications
A beginner tutorial to understand the theoretical and implementation details of gradient descent by backpropagation using Python.Assumptions/Recommendations: I assume you know matrix/vector math, introductory calculus (differentiation, basic understanding of partial derivatives), how basic feedforward neural nets work and know how to compute the output of a 2-layer neural net, and basic python/num
How to Create NBA Shot Charts in Python
%matplotlib inline import requests import matplotlib.pyplot as plt import pandas as pd import seaborn as sns Getting the data¶Getting the data from stats.nba.com is pretty straightforward. While there isn't a a public API provided by the NBA, we can actually access the API that the NBA uses for stats.nba.com using the requests library. This blog post by Greg Reda does a great job on explaining how
The Hipster Effect: An IPython Interactive Exploration | Pythonic Perambulations
This week I started seeing references all over the internet to this paper: The Hipster Effect: When Anticonformists All Look The Same. It essentially describes a simple mathematical model which models conformity and non-conformity among a mutually interacting population, and finds some interesting results: namely, conformity among a population of self-conscious non-conformists is similar to a phas
Kevin McAlear » Building Hater News | Thoughts
Hater News Haterz gonna hate. But now you know who the haterz are. I wanted to use data science / machine learning to identify and rank haters by their “hater” level throughout the internet. I started with Hacker News ( check out Hater News ) and I wanted to explain the how and what I’ve done so far. This post is long and detailed but I’ve tried to explain and post my code for how I’ve built out t
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Automating Microsoft Office with Python¶Windows applications, for many years, have provided a COM API for automation. This includes Microsoft Office as well. pywin32 is a library that lets you do many interesting things in Windows, including access these COM APIs. For example, to open PowerPoint and draw a circle, this is what it takes: import win32com.client # Open PowerPoint Application = win32c
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Happy Healthy Hungry -- San Francisco (H3)¶In this analysis, I will walk you through my general data science process by analyzing the inspections of San Francisco restaurants using publicly available data from the Department of Public health. We will explore this data to map the cleanliness of the city, and get a better perspective on the relative meaning of these scores by looking at statistics o
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https://louisabraham.github.io/notebooks/suffix_arrays.html
PyData.Tokyo Tutorial & Hackathon #1
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Using Python to find occurrences of Friday the�13th - Stacks
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