Why I started running.

-

I used to always come in first place when we had running races in the military.

After I got out of the military, I forgot about running for about 5 years. 

However, on a day when I had plenty of time and I was filled with thoughts, I started running by chance.

I've run since 2003. I started running along the Han River.

Running is a something which gives an energy into the body. Not only that, but running also stops my racing thoughts and gives my mind a rest.

Comments

Post a Comment

Popular posts from this blog

🧠 Building a Korean Character Recognition Model with PyTorch

-
Image
Hangul, the Korean writing system, is both elegant and systematic. Each character is a combination of components called Jamo: 초성 (initial), 중성 (medial), and 종성 (final). In this project, I built a deep learning model using PyTorch to recognize these components from rendered character images. Here's how I did it. ✨ Project Overview This project involves: Generating synthetic images of Hangul characters with their bounding boxes. Decomposing characters into Jamo components. Training a convolutional neural network to classify each character's 초성, 중성, and 종성. Performing inference on single or multiple images and visualizing the results. Let’s break it down. 🏗️ Step 1: Generate Hangul Character Dataset We use a TrueType font (like Malgun Gothic ) to render images of Hangul syllables and compute their bounding boxes. Each image is saved along with Jamo annotations in JSON. generate_korean_chars.py from PIL import Image , ImageDraw , ImageFont import os i...
Read more

🧠 Bayes' Theorem Interactive Game – Understand Conditional Probability Through Play

-
Bayes' Theorem Game This game helps you understand Bayes' Theorem: P(A|B) = [P(B|A) × P(A)] / P(B) You're a doctor testing for a disease. Fill in the values: P(Disease): P(Positive | Disease): P(Positive | No Disease): Calculate P(Disease | Positive) Explanation: P(A) = probability of the disease P(B|A) = probability of a positive test given disease P(B|¬A) = probability of a positive test without disease P(B) = total probability of a positive test P(A|B) = probability of having the disease if the test is positive Visualize the Effect of Each Variable 🧠 Bayes' Theorem Interactive Game – Understand Conditional Probability Through Play Bayes' Theorem is a powerful tool in probability theory that helps us reason about uncertain events. While it's often introdu...
Read more

🎯 Building an Object Detection Model for MNIST with PyTorch (COCO-style Dataset)

-
Image
Object detection is one of the core tasks in computer vision, and in this post, we'll walk through a complete pipeline: from downloading and converting the MNIST dataset to COCO format, to building and training a custom object detector in PyTorch. We'll also visualize predictions, plot training history, and analyze failure cases! This project demonstrates how to: Convert a dataset to the COCO format. Build a lightweight CNN-based object detector. Train and validate the model with bounding box and class predictions. Evaluate model performance visually and statistically. 🔧 1. Step-by-Step: Convert MNIST to COCO Format We’ll start by downloading MNIST and converting each digit into a COCO-style annotation: each image will have one bounding box and one label. 🗂 mnist_download.py import torch import torchvision from torchvision import datasets import os import json from PIL import Image import numpy as np from tqdm import tqdm import matplotli...
Read more