Scaling Transformers: A Practical Guide
This article explores methods and optimization techniques for efficiently scaling Transformer models in production environments.
Read MoreThis article explores methods and optimization techniques for efficiently scaling Transformer models in production environments.
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Read MoreA comprehensive overview of effective methodologies and key considerations for fine-tuning large language models for specific domains.
Read MoreA deep dive into the mathematical foundations and practical implementations of Diffusion Models, a core technology behind modern generative AI.
Read MoreA curated overview of essential tools and development environment setups that AI engineers rely on in their daily workflows.
Read MoreSpiking-based temporal models for sEMG-driven neuroprosthetic control.
Production-ready template for neuromorphic edge robotics (ROS2 + snnTorch + FastAPI + Docker).
In-progress latency-aware world model for long-horizon prediction in autonomous systems.
Browser-extension–driven job application assistant.
Hello, I’m an Applied AI Engineer who enjoys translating AI technologies into practical solutions for real-world challenges.
I am highly interested in building end-to-end AI systems, from machine learning model development to production deployment. In particular, I have a strong interest in Deep Learning, Natural Language Processing, and Computer Vision, and I enjoy solving the practical challenges that arise when applying state-of-the-art research to real-world services.
class AIEngineer:
def __init__(self):
self.name = "Junho Park"
self.role = "Applied AI Engineer"
self.interests = [
"Deep Learning",
"NLP",
"Computer Vision",
"MLOps"
]
def get_passion(self):
return "Building AI that matters"
# Initialize
engineer = AIEngineer()
print(engineer.get_passion())