Categories: AI/ML Research

A Complete Guide to Matrices for Machine Learning with Python

Matrices are a key concept not only in linear algebra but also with regard to their prominent application and use in machine learning (ML) and data science.

NumPy Ninjutsu: Mastering Array Operations for High-Performance Machine Learning

Machine learning workflows typically involve plenty of numerical computations in the form of mathematical and algebraic operations upon data stored as large vectors, matrices, or even tensors — matrix counterparts with three or more dimensions.

June 5, 2025

In "AI/ML Research"

A Gentle Introduction to Multi-Head Latent Attention (MLA)

This post is divided into three parts; they are: • Low-Rank Approximation of Matrices • Multi-head Latent Attention (MLA) • PyTorch Implementation Multi-Head Attention (MHA) and Grouped-Query Attention (GQA) are the attention mechanisms used in almost all transformer models.

June 24, 2025

In "AI/ML Research"

On a Neural Implementation of Brenier’s Polar Factorization

In 1991, Brenier proved a theorem that generalizes the polar decomposition for square matrices -- factored as PSD ×times× unitary -- to any vector field F:Rd→RdF:mathbb{R}^drightarrow mathbb{R}^dF:Rd→Rd. The theorem, known as the polar factorization theorem, states that any field FFF can be recovered as the composition of the gradient of…

July 17, 2024

In "FAANG"

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