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...

## build a simple recommender system with matrix factorization

We will build a recommender system which recommends top n items for a user using the matrix factorization technique- one of the three most popular used recommender systems. matrix factorization Suppose we have a rating matrix of m users and n items. The rating of user \(u_i\) to item \(i_j\) is \(r_{ij}\). Similar to PCA, matrix factorization (MF) technique attempts to decompose a (very) large...

## Sparse Matrices for Machine Learning quick note

In machine learning, many matrices are sparse. It is essential to know how to handle this kind of matrix. Sparse vs Dense Matrix First, it is good to know that sparse matrix looks similar to a normal matrix, with rows, columns or other indexes. But a sparse matrix is comprised of mostly zero (0s) values. They are distinct from dense matrices with mostly non-zero values. A matrix is sparse if many...

## TF2.0 Warm-up exercises (forked from @chipHuyen Repo)

Heard of Ms @huyen chip for her notable yet controversial travelling books back in the day. I enjoy reading but I am not really into travel memoirs. Nevertheless, she did surprise everyone by her achievements by getting in Stanford, teaching TensorFlow, and then became a computer/data scientist. Her story is definitely very inspiring. For ones who don’t know about Ms Huyen, I added an...