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SAMPLING Bagging vs Pasting

One way to get a diverse set of classifiers is to use very different training algorithms,as just discussed. Another approach is to use the same training algorithm for everypredictor but to train them on different random subsets of the training set. Whensampling is performed with replacement, this method is called bagging(short for bootstrap aggregating). When sampling is performed without...

Fast uniform negative sampling for rating matrix

petamind

Sometimes, we want to reduce the training time by using a subset of a very large dataset while the negative samples outnumbers the positive ones, e.g. word embedding. Another situation when we deal with implicit data. In this case, we may need to populate new data for negative values. This post demonstrates how to generate data for training using uniform negative sampling. The data Originally...

Best Android Image Loaders – 2022

chocolate cake on saucer beside iphone

In this article, we will explore some of the best Android image loaders for you. These are some of the most commonly used third-party libraries since Android SDK doesn’t provide a simple image loading solution for developers to use. Generally, you tend to have to implement one yourself or look at third-party libraries. Photo by Tim Gouw on Pexels.com Glide Glide is a fast and efficient open...

Understanding Latent Dirichlet Allocation (LDA)

petamind

Imagine a large law firm takes over a smaller law firm and tries to identify the documents corresponding to different types of cases such as civil or criminal cases which the smaller firm has dealt or is currently dealing with. The presumption is that the documents are not already classified by the smaller law firm. An intuitive way of identifying the documents in such situations is to look for...

MLP for implicit binary collaborative filtering

In this post, we demonstrate Keras implementation of the implicit collaborative filtering. We also introduce some techniques to improve the performance of the current model, including weight initialization, dynamic learning rate, early stopping callback etc. The implicit data For demonstration purposes, we use the dataset generated from negative samples using the technique mentioned in this post...

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