A technical deep dive into DeepSeek's Multi-Head Latent Attention mechanism, covering its mathematics and implementation in Julia.
Part 5 of a series on building an automatic differentiation package in Julia, demonstrating its use to create and train a multi-layer perceptron on the moons dataset.
Extends a Julia automatic differentiation library (MicroGrad.jl) to handle map, getfield, and anonymous functions, enabling gradient descent for polynomial fitting.
Explores using IRTools.jl for robust automatic differentiation in Julia, focusing on metaprogramming to generate forward and backward passes.
Explores automating automatic differentiation in Julia using metaprogramming and expression-based approaches to generate forward and backward passes.
An introduction to building a minimal automatic differentiation package in Julia, focusing on explicit chain rules and the Julia AD ecosystem.
A tutorial on building a generative transformer model from scratch in Julia, trained on Shakespeare to create GPT-like text.
A guide to implementing a radix tree (compressed trie) data structure in Julia using Test Driven Development (TDD).
A beginner-friendly tutorial explaining what macros are in Julia, how they differ from functions, and guidance on when to use them.
Addresses common misconceptions about Julia's performance, explaining its JIT compilation, startup time, and best practices for optimal speed.
A technical exploration of multiple ways to access a single element from a 1x1 array in Julia, including performance benchmarks.
Analysis of documentation, testing, and CI adoption in Julia packages from the General registry, using a custom tool.
Introduces RandomBasedArrays.jl, a Julia package that uses random starting indices for arrays to sidestep the 0 vs. 1-based indexing debate.
Explores how multiple dispatch in Julia enables mathematically correct code, using complex number division as a key example.
Explains how to dereference pointers and access C library data in Julia using functions like unsafe_load and unsafe_wrap.
Compares the Julia do-block and Python with statement, explaining their different purposes in resource management and function passing.
Cosmology.jl v0.5.0 now integrates with Unitful.jl, adding physical units to calculations and enabling uncertainty propagation with Measurements.jl.
Introducing AstroImages.jl, a Julia package for visualizing astronomical images from FITS files.
Explores how assignment expressions in Julia return a value, similar to C, and discusses its uses and potential pitfalls.
Explains the Bailey-Borwein-Plouffe formula for computing hexadecimal digits of π and provides a Julia implementation.
