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Thoughts on AI, machine learning, and software engineering. Tutorials, research insights, and lessons learned.

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FeaturedDeep Learning

Understanding Vision Transformers: A Deep Dive

Exploring the architecture and mechanisms that make ViT models so effective for image classification tasks. We break down attention patterns and positional encodings.

·8 min read
FeaturedTutorial

Fine-tuning LLMs with LoRA: A Practical Guide

Learn how to efficiently fine-tune large language models using Low-Rank Adaptation techniques. Includes code examples and best practices.

·12 min read

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MLOps

Building Production-Ready ML Pipelines

Best practices for creating scalable, maintainable machine learning pipelines in production environments. From data ingestion to model serving.

·10 min read
Deep Learning

The Mathematics Behind Attention Mechanisms

A mathematical deep dive into self-attention, multi-head attention, and their computational complexity. Understanding the theory behind transformers.

·15 min read
MLOps

Efficient Inference with TensorRT

Optimizing neural network inference using NVIDIA TensorRT. Learn about quantization, layer fusion, and dynamic batching.

·9 min read
Research

My Journey to NeurIPS 2024

Reflections on publishing my first paper at a top-tier conference. The research process, rejections, and lessons learned.

·7 min read