What makes Numpy Arrays Fast: Memory and Strides

Jessica YungMachine Learning, ProgrammingLeave a Comment

How is Numpy so fast? In this post we find out how Numpy’s ndarray is stored and how it is usually manipulated by Numpy functions using strides. Getting to know the ndarray A NumPy ndarray is a N-dimensional array. You can create one like this:

These arrays are homogenous arrays of fixed-sized items. That is, all the items in … Read More

MSE as Maximum Likelihood

Jessica YungMachine LearningLeave a Comment

MSE is a commonly used error metric. But is it principly justified? In this post we show that minimising the mean-squared error (MSE) is not just something vaguely intuitive, but emerges from maximising the likelihood on a linear Gaussian model. Defining the terms Linear Gaussian Model Assume the data is described by the linear model , where . Assume is … Read More

Maximum Likelihood as minimising KL Divergence

Jessica YungMachine LearningLeave a Comment

Sometimes you come across connections that are simple and beautiful. Here’s one of them! What the terms mean Maximum likelihood is a common approach to estimating parameters of a model. An example of model parameters could be the coefficients in a linear regression model , where is Gaussian noise (i.e. it’s random). Here we choose parameter values that maximise the … Read More