In machine learning it is pretty obvious to me that you need to split your dataset into 2 parts: a training set that you can use to train your model and find optimal parameters a test set that you can use to test your trained model and see how well it generalises. It is important […]
Most machine learning techniques follow a similar strategy: Get the best possible model on the training dataset Generalise by testing the model on the test dataset The test dataset consists of data that are never used during training and it allows to test how the algorithm will perform over “not seen before” data.
With gradient descent we try to optimise a function that runs over the entire dataset. represents the “cost” over the entire dataset. When working with big datasets this yield to complex function optimisation and slow computation time. This is also a problem when dealing with streaming data as we need to wait for the stream to end […]
If you want to predict something from your data, you need to put a strategy in place. I mean you need a way to measure how good your predictions are … and then try to make the best ones. This is usually done by taking some data for which you already know the outcome and […]
PCA stands for Principal Component Analysis. It is a mathematical concept which I am not going to explain in great details here as there are already plenty of books on the subject. Rather I would like to give a practical feeling of what it does and when to use it. The idea behind PCA is that […]