IMTI

This article implements neural network foundations in Go using gonum: a perceptron, forward propagation, and backpropagation from scratch.

This article implements Principal Component Analysis (PCA) from scratch in Go using gonum, covering both the covariance matrix and SVD approaches.

This article demonstrates building a regression library in Go from scratch using gonum: ordinary least squares, ridge regression, and cross-validation.

This article covers practical machine learning applications, the final part of the series. I’ll show how the linear algebra concepts from previous articles apply to neural networks, gradient computation, and efficient vectorized operations.

This article covers Principal Component Analysis (PCA), part eleven of the series. PCA is one of the most widely used techniques for dimensionality reduction, data visualization, and feature extraction in machine learning.

This article covers Singular Value Decomposition (SVD), part ten of the series. SVD is arguably the most important matrix decomposition, with applications in image compression, recommender systems, pseudoinverse computation, and dimensionality reduction.

This article covers least squares and regression, part nine of the series. Least squares is one of the most important applications of linear algebra and forms the foundation of regression analysis used throughout data science and machine learning.