- Fundamentals of Deep Learning by NVIDIA
- Transformer Based Natural Language Processing Models by NVIDIA
- Microsoft Certified: Azure AI Fundamentals by Microsoft
- Microsoft Certified: Azure Data Fundamentals by Microsoft
- Received a B.S. degree in Artificial Intelligence from Jeonju University, Jeonju, Korea, in 2025.
- Pursuing an M.S. degree in Agro AI at Jeonju University, Jeonju, Korea, in 2024 ~ Present.
- Completed an education certificate program at the University of Toronto's C-MORE Lab.
- Worked as a Research Intern at Dareesoft through the WEMEET program.
- Worked as a Research Intern at the Rural Development Administration through the WEMEET program.
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Large Language Model (LLM) Optimization
- Optimizing retrieval models for faster and more accurate augmented generation
- Applying Low-Rank Adaptation (LoRA) to fine-tune large-scale models efficiently.
- Parameter-Efficient Fine-Tuning (PEFT)
- Time-Series data Anomaly Detection Model
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Generative Models using Deep Learning
- GAN-Based Models
- Generative Adversarial Networks (GANs)
- Conditional GAN (cGAN)
- CycleGAN
- StyleGAN / StyleGAN2
- Autoencoder-Based Models
- Variational Autoencoder (VAE)
- Vector Quantized VAE (VQ-VAE)
- Denoising Autoencoder
- Sparse Autoencoder
- Diffusion-Based Models
- Denoising Diffusion Probabilistic Models (DDPMs)
- Score-Based Generative Models
- Vector Quantized Diffusion Models (VQ-DM)
- Conditional Diffusion Models with Quantization (CDM-Q)
- Discrete Diffusion Models (DDM)
- GAN-Based Models
-
Image Segmentation using Deep Learning
- Supervised Image Segmentation
- U-Net
- DeepLab (DeepLabV3, DeepLabV3+)
- Mask R-CNN
- PSPNet (Pyramid Scene Parsing Network)
- SegNet
- Unsupervised Image Segmentation
- Mean Shift Segmentation
- Deep Embedded Clustering (DEC)
- Segment Anything Model (SAM)
- Supervised Image Segmentation
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Image Anomaly Detection
- Out-of-Distribution Detection
- Defect Generation
- Feature-Embedding Based Methods
- Reconstruction Based Methods
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Paper Reading and Research
- Code for Research
- Paper Reading
- A Survey of Methods for Brain Tumor Segmentation Based on MRI Images
- Edge U-Net: Brain Tumor Segmentation Using MRI Based on Deep U-Net Model with Boundary Information
- FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net
- Using a Generative Adversarial Network to Generate Synthetic MRI Images for Multi-class Automatic Segmentation of Brain Tumors
- Edge-Boosted U-Net for 2D Medical Image Segmentation
- A survey on efficient vision transformers: algorithms, techniques, and performance benchmarking
- others in here
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- linkedin: https://www.linkedin.com/in/yurim-oh-96709516a/
- email: [email protected]
### Programming Languages
- Python
### Jupyter Notebooks
- Jupyter Notebook
### Deep Learning Frameworks
- PyTorch
- TensorFlow
- Keras
### Data Science Libraries
- Pandas
- NumPy
### Computer Vision Libraries
- OpenCV (cv2)
- Ultralytics
### Plotting Libraries
- Matplotlib (matplotlib)
### Machine Learning Libraries
- Scikit-learn (sklearn)
### Natural Language Processing
- LangChain
- LlamaIndex