
Understanding Transformer Architectures in Modern NLP
A deep dive into the attention mechanism and how transformers revolutionized natural language processing, from BERT to GPT and beyond.
Insights and deep dives into machine learning, artificial intelligence, and software engineering

A deep dive into the attention mechanism and how transformers revolutionized natural language processing, from BERT to GPT and beyond.

Lessons learned from deploying machine learning models at scale, including CI/CD pipelines, monitoring, and infrastructure considerations.
Exploring how deep learning techniques can be applied to optical coherence tomography images for diabetic retinopathy diagnosis.

How contrastive learning enables models to perform NLI tasks without task-specific training data, with practical implementations.
A practical guide to setting up event-driven ML pipelines using Apache Kafka, Docker containers, and microservices architecture.
From prompt tuning to parameter-efficient methods, explore different approaches to adapt LLMs for specific tasks and domains.