A.I, Data and Software Engineering

# Articles

## The intuition of Principal Component Analysis

Table of contentsPCA intuitionPCA procedureAppling PCA with Iris datasetHow much information preservedConclusion 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...

## deep learning: Linear Autoencoder with Keras

Table of contentsWhat is a linear autoencoderother variationsWhen to useexample with Keras and TF.2.xBuild an autoencoder modelTrain the modelGet the encoded data 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...

## Math for ML – Vector norms quick note

Table of contentsFrom a high school entrance exam…Vector normsManhattan norm – l1EUCLIDEAN NORM – L2p-norm ($$L^p$$ spaces)Frobenius normQuick note REFERENCE: 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...

## Sparse Matrices for Machine Learning quick note

Table of contentsSparse vs Dense MatrixProblems with SparsitySpace ComplexityTime ComplexitySparse Matrices in Machine LearningDataData PreparationAreas of StudyWorking with Sparse MatricesSparse Matrices in PythonCSR/CSC operatorsConclusion: 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...

## Recurrent neural network – predict monthly milk production

Table of contentsThe dataPreprocess the training dataCreate batch training dataSetting Up The RNN ModelThe milk PredictionTo sum up In part 1, we introduced a simple RNN for time-series data. To continue, this article applies a deep version of RNN on a real dataset to predict monthly milk production. The data Monthly milk production: pounds per cow. Jan 1962 – Dec 1975. You can download the...

A.I, Data and Software Engineering

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