# Authortungnd

Got a bachelor of Telecommunication Engineering from Shanghai University (China), Master of Information, Systems, and Technologies from Paris Sud 11 (France), and PhD of Computer Science from Auckland University of Technology. Current research interests include multi-agent systems (A.I), data science, and software engineering.

## One-hot encoding matrices demonstration This post will demonstrate onehot encoding for a rating matrix, such as movie lens dataset. One-hot encoding Previously, we introduced a quick note for one-hot encoding. It is a representation of categorical variables as binary vectors. It is a group of bits among which the legal combinations of values are only those with a single high (1) bit and all the others low (0) Rating matrix If you are...

## 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...

## The intuition of Principal Component Analysis As PCA and linear autoencoder have a close relation, this post introduces again PCA as a powerful dimension reduction tool while skipping many mathematical proofs. PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly...

## deep learning: Linear Autoencoder with Keras This post introduces using linear autoencoder for dimensionality reduction using TensorFlow and Keras. What is a linear autoencoder An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network...

## Math for ML – Vector norms quick note Vector norms are used in many machine learning and computer science problems. This article covers some common norms and related applications. From a high school entrance exam… Remember the day (?/?/1998) when I took an exam to a high school, there was a problem of finding the shortest path from A to B knowing that the person can only go left/right or up/down given the following grid of m x...

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