# Sigmoid for Classifiers Decoded

Hello World,

Sigmoid really isn’t that complicated (once your understand it of course).  Some back knowledge in case you are coming at this totally fresh is that the Sigmoid function is used in machine learning primarily as a hypothesis function for classifiers.  What is interesting is that this same function is used for binary classifiers, multi-class classifiers and is the backbone of modern neural networks.

Here is the sigmoid function:    $\frac{ 1 }{ 1 + e^{-z}}$

# Getting Started with Linear Algebra in Python

Hello World!

So here I am after trying for a long time to not learn Python learning Python.  It just seems like I might get a hit or two more on my blog with some Python content.  Well whats the first thing I need to figure out aside from getting it up and running in my environment and installing some libraries… Thats right, find a numerical computing library and see how it ticks.

Hello World!

In this article we are going to cover a simple version of Gradient Descent. It is important to note that this version of gradient descent is using Sum of Squares as its cost function to reduce. This implementation utilizes vectorized algorithms. Lets start off with…

# Machine Learning Study Group – Week 2 & 3 Recap

Hello World,

So this is another recap from our study group covering the Andrew NG course on Coursera. Lets start by a quick summary from the two weeks. Week 1 was all about introduction to linear regression and gradient descent. There were no assignments due. Week 3 was all about multi-variate linear regression, normalization and a few other topics. There was a coding assignment as well as a quiz due for week 2.

# Feature Scaling & Machine Learning

Hello World!

If you are practicing machine learning, you are likely going to run into this at some point.  Basically the reason we use feature scaling is to help our algorithms train faster and better.  Lets begin by taking a standard theta optimization equation to help better understand the problem.
$\theta_j = \theta_j - \alpha \cdot \frac{ \sum_i^m \left(H_{\theta}\left(x\right) - y\right) \cdot x_j } { m }$

# Machine Learning Study Group Recap – Week 1

Hello World!

So many of you who are here are probably part of the study group.  For those who are not or are perhaps referencing this at a later time, this is in regards to the following course on Coursera. If you would like to join our study group, please see one of the following meetup pages: Fort Lauderdale Machine Learning or Florida Dot Net.

Here in South Florida we have a strong Machine Learning and Data Science community and therefor it is easy to get a study group together.  This article is a recap from the first meeting of our study group.  Note that this first meeting is the week before the class started.  Therefor this article is a great introduction to machine learning, languages, commitments and more generally applicable questions and concerns.

# Linear Regression from Scratch

Hello World,

So today we will do a quick conversion from mathematical notations of Algebra into a real algorithm that can be executed.  Note we will not be covering gradient descent, but rather only cost functions, errors and execution of these to provide the framework for gradient descent.  Gradient descent has so many flavors that it deserves its own article.

So to the mathematical representation.