A.I, Data and Software Engineering

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One-hot encoding matrices demonstration

movie lens automation

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...

One-hot encoding quick note

Petamind A.I

Quickly grasp the concept of one-hot encoding by simple data and coding. Categorical VS Numerical data Categorical data are variables that contain label values. For example, A “colour” variable can have values “red“, “green” and “blue“. Here, “red”, “green”, “blue” are labels represented by strings. Numerical data are...

Feature Engineering FundamentalS

Petamind A.I

The features you use influence more than everything else the result. No algorithm alone, to my knowledge, can supplement the information gain given by correct feature engineering.— Luca Massaron What is a feature and why we need engineering of it? Basically, all machine learning algorithms use some input data to create outputs. This input data comprise features, which are usually in the form...

Word2vec with TensorFlow 2.0 – a simple CBOW implementation

Petamind A.I

In TensorFlow website, there is a good example of word embedding implementation with Keras. Nevertheless, we are curious to see how it looks like when implementing word2vec with PURE TensorFlow 2.0. What is CBOW In the previous article, we introduced Word2vec (w2v) with Gensim library. Word2vec consists of two-layer neural networks that are trained to reconstruct linguistic contexts of words. The...

Decision Tree Regression quick note

Petamind A.I

Decision tree builds regression or classification models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. A decision node (e.g., Outlook) has two or more branches (e.g., Sunny, Overcast and Rainy), each representing...

A.I, Data and Software Engineering

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