Keras

I am hoping to get back to some core posts on automating office with python soon, however at the moment I am exploring machine learning and neural networks as I personally have some situations where they would be useful to implement, at work I have some use cases and lets face it they are cool. 

In order to improve my home portfolio of machine learning and data science techniques I have been looking into Keras and using it to build a neural network. My hope is that over the next week I can replace the logistic regression model I build for predicting the odds of Dupuytren's patient developing Ledderhose with a Neural network. 

Starting out with Python and Neural Networks:

Ok so when you are first looking into Neural Networks for python you start hearing words like Keras, Tensorflow and Theano.

What is Keras?

Well Keras is a library that sits on top of other machine learning libraries such Tensorflow and Theano. To quote the Wikipedia page "Keras was conceived to be an interface rather than an end-to-end machine-learning framework. It presents a higher-level, more intuitive set of abstractions that make it easy to configure neural networks regardless of the backend scientific computing library."

The Keras homepage actually has a get started in 30 seconds section and although not with my dataset but a sample one I did get a model up and running in a sleep deprived state. It really is simple, I also found the video here.

So Keras does lots of clever stuff to make it easier and many less lines of code to run process in Tensorflow. In fact above is the output given when you kick off Keras.

I have only really give a brief intro in this post however I am hoping have a great post ready soon with my first publishable neural network in Keras.