이복연 Lee
Distinguished Editorial Specialist at Hanbit Media | 한빛미디어
Seoul, South Korea
Summary
🤩
Rockstar🎓
Top School이복연 Lee is a distinguished editorial specialist and former software engineer with over two decades of hands-on experience bridging technical depth and publishing expertise. Currently at Hanbit Media, he has shaped IT and developer-focused content—editing Korean editions of influential titles like Deep Learning from Scratch and Effective Java—while earlier roles include founding a tech publishing startup and engineering positions at Samsung. His background spans low-latency server frameworks, cloud auto-scaling, mobile platform architecture, and early J2ME apps, giving him an unusual blend of systems-level engineering and precise technical communication. On GitHub he translated and clarified code examples for the well-known "Deep Learning from Scratch" repository, reflecting a commitment to making complex ML concepts accessible. Colleagues value his ability to translate intricate technical material into readable, teachable content without losing rigor. Based in Seoul with graduate studies in computer science from Korea University, he combines editorial leadership with practical engineering pedigree.
9 years of coding experience
14 years of employment as a software developer
Graduate, Computer Science, Graduate, Computer Science at Korea University
English, Korean
Stackoverflow
11reputation
1kreached
0answers
1question
Github Skills (10)
machine-learning10
deep-learning10
python10
numpy10
matplotlib9
mnist8
doctrine6
doctrine-orm6
one-to-many6
yaml6
Programming languages (4)
Github contributions (5)
WegraLee/deep-learning-from-scratch
Dec 2016 - Nov 2020
『밑바닥부터 시작하는 딥러닝 1(리마스터판)』(한빛미디어, 2025)
Role in this project:
Data Scientist
Data ScientistContributions:144 commits, 219 pushes in 3 years 11 months
Contributions summary:이복연 primarily contributed by translating code comments within the "deep-learning-from-scratch" repository. These translations span across multiple chapters, including code related to basic neural networks, image manipulation, and the MNIST dataset. The user also made minor code adjustments, such as changing ranges for plotting graphs. This work indicates a focus on understanding and clarifying the code examples within the deep learning context.
pytorchdeep-learningmachinelearningtensorflowmnist
WegraLee/deep-learning-from-scratch-2
Mar 2019 - Aug 2021
『밑바닥부터 시작하는 딥러닝 ❷』(한빛미디어, 2019)
Contributions:69 commits, 75 pushes in 2 years 5 months
deep-learningpytorchtensorflow
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