neural networks

Machine Learning on a 'Real Problem' - Part 2: Using the Right Model

Machine Learning on a 'Real Problem' - Part 2: Using the Right Model

Review

Our last classifier was very poor--it operated at chance--a coin flip would have had the same predictive power. A few things may have been going on that caused us to find no signal. It could be that we down sampled our images too much and lost useful information, it could be that our neural network was poorly configured (it was), it could be we were using the wrong type of neural network (we were), or it could be all of these. To address all of these issues, we'll spend a little more time constructing model this time--examining the underlying construct of the data itself, what we really want to learn from it, and how best to model that.

We’ll be using keras to interface with TensorFlow, and a type of Recurrent Neural Network (RNN) called a Long Short-Term Memory Network (LSTM).