Deep Learning

A sleek laptop screen displaying Python code with a GPT model diagram.

Hands-On: Scratch‑Built GPT Training Loop with Activation Recomputation in Pure Python

A minimal, pure‑Python GPT training loop with gradient checkpointing, perfect for showcasing systems‑level deep learning know‑how on a CV.

A visualization of a Mixture-of-Experts architecture with sparse routing paths

Build a Sparse MoE Inference Engine from Scratch in NumPy

A hands-on guide to building a sparse MoE inference engine in pure NumPy, covering top-k gating, capacity-constrained token dispatch, and differentiable load balancing — a portfolio project that signals deep systems and ML engineering skill.

A visualization of ring attention with tokens flowing in a circular topology across GPU devices.

Build a Ring Attention Engine: A Hands-On Guide for Signal-Level Systems Engineers

A hands-on build guide for implementing ring attention — the technique behind near-infinite context transformers — as a portfolio project that signals deep distributed systems and ML engineering skill to hiring managers.

GPU computing visualization with sparse matrix data flow

Optimizing CUDA Kernels for Sparse Matrix Multiplication: A Performance Guide

Sparse matrix multiplication is a bottleneck in many GPU workloads. This guide covers storage formats, kernel-level optimization strategies, and profiling techniques to extract maximum performance from your CUDA kernels.

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Implementing CUDA Graphs for Low-Latency GPU Inference Pipelines

CUDA Graphs capture and replay GPU workloads, reducing kernel launch overhead and cutting inference latency by up to 40% in production systems.