When working on machine learning problems, sometimes you want to construct your own custom loss function(s). This article will introduce abstract Keras backend for that purpose. Keras loss functions From Keras loss documentation, there are several built-in loss functions, e.g. mean_absolute_percentage_error, cosine_proximity, kullback_leibler_divergence etc. When compiling a Keras model, we often...
It has been a while since I joined Stack Overflow. As a programmer, I would like to acknowledge the creators and all contributors to the website. It has been an awesome resource for developers and other fields of interest. Stack Overflow is a question-and-answer site for professional and enthusiast programmers. It is a privately-held website, the flagship site of the Stack Exchange...
Topic modelling is a type of statistical modelling for discovering the abstract “topics” that occur in a collection of documents. Latent Dirichlet Allocation (LDA) is an example of a topic model and is used to classify text in a document to a particular topic. It builds a topic per document model and words per topic model, modelled as Dirichlet distributions. Here, we are...
These are completely different methods in machine learning. The fact that they both have the letter K in their name is a coincidence. K-means is a clustering algorithm that tries to partition a set of points into K sets (clusters) such that the points in each cluster tend to be near each other. It is unsupervised because the points have no external classification. The typical k-means...
Lasso, Ridge, and Elastic Net are excellent methods to improve the performance of your linear model. This post will summarise the usage of these regularization techniques. Bias: Biases are the underlying assumptions that are made by data to simplify the target function. Bias does help us generalize the data better and make the model less sensitive to single data points. It also decreases the...