In the previous post we covered the hypothesis function, which is the function that will predict a value given a set of features for a new unknown case. In this post we’re going to build the cost function, a way to mearsure the error of the prediction function with a specific set of weights .
Python is becoming the defacto standard for Big Data and Machine Learning, in particular this is because of some amazing tools like IPython Notebook that helps visualizing your data or scikit learn that implement some of the most popular machine learning algorithms.
So implementing a ML algorithm in go is a pure excercise.
This is the situation: the team is very small, just 3 people in the engineering department. It’s a young startup in a early stage with a very tight budget and the engineering team is building the product as a side project, aka in the spare time.
If it wasn’t enough the team is spread in different cities, so being in the same room frequently is impossible. It’s pretty obvious that the team cannot follow a regular scrum approach to development.
Bitcoin sign has been accepted into unicode. Here’s the symbol: ₿ (it will work someday)
People who know me is aware of my aversion against social networks and blogs. So why a blog?
I’m thinking of this space as a personal set of notes and thoughts about technical things that I can share with friends and colleagues.