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noviceforever
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  • About me
  • Miscellaneous
    • Introduction
      • ์ปค๋ฆฌ์–ด ์š”์•ฝ
      • ๋ฐ์ดํ„ฐ ๊ณผํ•™์ด๋ž€?
  • Machine Learning
    • Tabular Data
      • XGBoost Algorithm Overview
      • TabNet Overview
      • Imbalanced Learning
        • Introduction
        • Oversampling Basic (SMOTE variants)
        • Cost-sensitive Learning
        • RBF(Radial Basis Function)-based Approach
    • Computer Vision (CNN-based)
      • [Hands-on] Fast Training ImageNet on on-demand EC2 GPU instances with Horovod
      • R-CNN(Regions with Convolutional Neuron Networks)
      • Fast R-CNN
      • Faster R-CNN
      • Mask R-CNN
      • YOLO (You Only Look Once)
      • YOLO v2(YOLO 9000) Better, Faster, Stronger
      • YOLO v3
      • SSD (Single Shot Multibox Detector)
      • Data Augmentation Tips
    • Computer Vision (Transformer-based)
      • ViT for Image Classification
      • DeiT (Training Data-efficient Image Transformers & Distillation through Attention)
      • DETR for Object Detection
    • Natural Language Processing
      • QRNN(Quasi-Recurrent Neural Network)
      • Transformer is All You Need
      • BERT(Bi-directional Encoder Representations from Transformers)
      • DistilBERT, a distilled version of BERT
      • [Hands-on] Fine Tuning Naver Movie Review Sentiment Classification with KoBERT using GluonNLP
      • OpenAI GPT-2
      • XLNet: Generalized Autoregressive Pretraining for Language Understanding
    • Recommendation System
      • Recommendation System Overview
      • Learning to Rank
      • T-REC(Towards Accurate Bug Triage for Technical Groups) ๋…ผ๋ฌธ ๋ฆฌ๋ทฐ
    • Reinforcement Learning
      • MAB(Multi-Armed Bandits) Overview
      • MAB Algorithm Benchmarking
      • MAB(Multi-Armed Bandits) Analysis
      • Policy Gradient Overview
    • IoT on AWS
      • MXNet Installation on NVIDIA Jetson Nano
      • Neo-DLR on NVIDIA Jetson Nano
    • Distributed Training
      • Data Parallelism Overview
    • Deployment
      • MobileNet V1/V2/V3 Overview
      • TensorRT Overview
      • Multi Model Server and SageMaker Multi-Model Endpoint Overview
  • AWS AIML
    • Amazon Personalize
      • Amazon Personalize - User Personalization Algorithm Deep Dive
      • Amazon Personalize Updates(~2021.04) ๋ฐ FAQ
    • Amazon Bedrock AgentCore
      • AgentCore Runtime
      • AgentCore Gateway
      • AgentCore Identity
      • AgentCore Observability
      • AgentCore Code Interpreter
      • AgentCore Browser
      • AgentCore Memory
    • Customer Support
      • GenAI System Checklist
  • GenAI
    • Theory
      • [Paper Review] Zero-Shot Text-to-Image Generation (DALL-E)
      • [Paper Review] Diffusion ๊ฐœ๋… ์ •๋ฆฌ
    • Synthetic Data
      • Part 1. ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ์˜ ํ•„์š”์„ฑ
      • Part 2. Seed ๋ฐ์ดํ„ฐ ๊ธฐ๋ฐ˜ ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ ์ƒ์„ฑ ์ ‘๊ทผ ๋ฐฉ์‹
      • Part 3. Seedless ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ ์ƒ์„ฑ ์ ‘๊ทผ ๋ฐฉ์‹
      • Part 4. ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ ์ƒ์„ฑ ์‹œ ๊ณ ๋ คํ•ด์•ผ ํ•  ์ผ๋ฐ˜์ ์ธ ์ „๋žต
      • [Paper Review] 10์–ต ํŽ˜๋ฅด์†Œ๋‚˜ ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ
      • [Use-case w/ Hands-on] ์‹ค์ œ ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ ํ•ฉ์„ฑ QnA ์ƒ์„ฑํ•˜๊ธฐ
    • MoE (Mixture-of-Experts)
      • MoE Overview
    • Open Source SLM-Based Hybrid Agent AI Architecture
      • Part 1: Overview & Background
      • Part 2: Agentic Patterns & Prompting
      • Part 3: Tool Integration & Fine-Tuning
    • Fine-tuning
      • [Use-case w/ Hands-on] Azure ML์—์„œ torchtune์„ ์‚ฌ์šฉํ•œ ์–ธ์–ด ๋ชจ๋ธ ๋ฏธ์„ธ ์กฐ์ •/ํ‰๊ฐ€/์–‘์žํ™”
      • [Use-case w/ Hands-on] Azure ML Python SDK ๋ฐ MLflow๋ฅผ ํ™œ์šฉํ•œ Florence-2 ๋ชจ๋ธ ํŒŒ์ธ ํŠœ๋‹
    • LLM Evaluation
      • Overview
      • ํ•œ๊ตญ์–ด LLM ํ‰๊ฐ€์˜ ๋‚œ์ œ
      • [Paper review] KMMLU/KMMLU-Redux/KMMLU-Pro Dataset
      • [Paper review] FunctionChat-Bench
      • ํ˜ธ๋ž‘์ด ํ•œ๊ตญ์–ด LLM ๋ฆฌ๋”๋ณด๋“œ
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  1. GenAI

MoE (Mixture-of-Experts)

MoE Overview
Previous[Use-case w/ Hands-on] ์‹ค์ œ ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ ํ•ฉ์„ฑ QnA ์ƒ์„ฑํ•˜๊ธฐNextMoE Overview

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