Timeline

[Generative Recommendation] (13) EAGER (2): 두 흐름이 서로 배우게 하는 보조 과제

[Generative Recommendation] (11) LC-Rec (2): 언어와 협업 의미를 잇는 정렬 과제

[Generative Recommendation] (9) TokenRec (2): 아이템 토큰을 생성하는 대신 검색하기

[Generative Recommendation] (7) LETTER: 협업 신호를 담는 학습형 토크나이저

[Generative Recommendation] (5) 좋은 Semantic ID의 조건: 무엇을 양자화하고, 얼마나 길게 만들고, 어떻게 평가할까

[Generative Recommendation] (3) 벡터 양자화와 VQ-VAE: 코드북 학습, Straight-Through Estimator, 그리고 코드북 붕괴

[Generative Recommendation] (1) 왜 생성형 추천인가: 점수를 매기던 추천에서 아이템을 생성하는 추천으로

[논문 리뷰] Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators

[Machine Learning] SMOTE : 불균형 데이터 합성 샘플링 가이드

[Machine Learning] Layer Normalization Tuning

[Docker] Docker Compose Cheatsheet

[Docker] Docker Cheatsheet

[Machine Learning] Parquet vs Arrow

[Python] uv : 패키지 관리 도구

[Python] PEP 20 : The Zen of Python

[ML] Titans: Learning to Memorize at Test Time by Google

[Python] FastAPI Uvicorn + Gunicorn

[Linux] Tmux 사용법 및 주요 명령어

[MLOps] Kubeflow Pipiline 소개

[논문 리뷰] Trustworthy Residual Vehicle Value Prediction for Auto Finance

[논문 리뷰] Bootstrap Latent Representations for Multi-modal Recommendation

[Recommendation System] 추천 모델 개발 (3) - 딥러닝 모델

[Recommendation System] 추천 모델 개발 (2) - 딥러닝 모델

[Recommendation System] 추천 모델 개발 (1) - 모델 데이터

[Recommendation System] 추천 모델

[Recommendation System] 추천 서비스란 무엇인가

[Recommendation System] 연관규칙분석(Apriori Algorithm)

[Data Cleaning] 05. Inconsistent Data Entry (일관되지 않은 데이터 입력)

[Data Cleaning] 04. Character Encodings (문자 인코딩)

[Data Cleaning] 03. Parsing Dates (날짜 파싱)

[Data Cleaning] 02. Scaling and Normalization (스케일링 및 정규화)

[Time Series] 05. Hybrid Models

[Time Series] 04. Time Series as Features

[Time Series] 03. Seasonality

[Time Series] 02. Trend

[LLM] Pretrained vs Fine-tuned vs Instruction-tuned vs RL-tuned

[LLM] sLLM(small Large Language Model)

[ML] ANN, DNN, CNN, RNN의 개념과 차이

[TensorFlow] Recurrent Neural Networks for Time Series (2)

[TensorFlow] Recurrent Neural Networks for Time Series (1)

[TensorFlow] Deep Neural Networks for Time Series (3)

[TensorFlow] Deep Neural Networks for Time Series (2)

[TensorFlow] Deep Neural Networks for Time Series (1)

[ML] 라벨 인코딩과 원핫 인코더를 사용한 범주형 인코딩

[BI] Power BI

[LangChain] 모듈(Modules)

[LangChain] LangChain, Chat with Your Data

[Amazon Sagemaker] Amazon SageMaker 모델 빌딩 파이프라인 소개

[Database] Concurrency Control

[Generative AI with Large Language Models] Introduction to LLMs and the generative AI project lifecycle (2)

[Generative AI with Large Language Models] Introduction to LLMs and the generative AI project lifecycle

[LLM] Meta LLaMA의 친척 — Stanford Univ의 Alpaca

[LLM] Meta AI의 Small Gaint Model: LLaMA(Large Language Model Meta AI)

[Markdown] 마크다운 표(Table) 작성하는 법

[Markdown] 마크다운 문서 내부 링크 이동 (북마크, 바로 가기, 목차)

[Markdown] 마크다운 작성 방법

[Data Visualization] Reduce and Focus+Context

[Database] Storage and Basic Files

[Data Visualization] Validation

[Data Visualization] Arrange Table

[Database] SQL (Structured Query Language) - 2

[Data Visualization] Colors (Perception)

[Database] Relational Schema Design ER to Relational Mapping

[Data Visualization] Marks and Channels

[Data Visualization] Why - Task Abstraction

[Data Visualization] What - Data Abstraction

[Data Visualization] Justification

[Data Visualization] Introduction to InfoVis (2)

[Data Visualization] Introduction to InfoVis (1)

[Spring Boot] 스프링 부트 배포하기 (Maven, Gradle) War 파일로 빌드

[Java] 그레이들(Gradle) 개념

[Artificial Intelligence] Reinforcement Learning

[Operating System] Virtual Memory Management

[Data Structures & Algorithms] Sorting (1)

[Spring] Batch 소개와 간단한 예제

[Spring] Batch 구조와 구성 요소

[Artificial Intelligence] 퍼셉트론(Perceptiron)

[Programming] REST란? REST API 와 RESTful API의 차이점

[Programming] HTTP 상태 코드 정리

[Operating System] Memory Management

[Data Structures & Algorithms] Binary Search Tree

[Artificial Intelligence] Strong Method

[Operating System] Process Management (2)

[Data Structures & Algorithms] Basic Concepts

[Artificial Intelligence] Heuristic search

[Artificial Intelligence] Adversarial Search

[Operating System] Introduction and System Structures

[Artificial Intelligence] Introduction to Aritificial Intelligence

[Python] 제너레이터(Generator)

[ML] BERT Fine-Tuning

[Machine Learning] BERT, GPT-2

[Machine Learning] 트랜스포머(Transformer) - 트랜스포머와 컴퓨팅

[NLP] Word2Vec

[NLP] 단어 표현 (Word Representation)

[NLP] 토큰화(Tokenization) / 토크나이징(Tokenizing)

[NLP] 텍스트 데이터의 문자열 처리

[NLP] 정규 표현식을 이용한 문자 추출

[Machine Learning] Attention Is All You Need (1)

[Ⅴ. Sequence Models] Transformer Network (1)

[Ⅴ. Sequence Models] Sequence Models & Attention Mechanism (1)

[Ⅴ. Sequence Models] Natural Language Processing & Word Embeddings (5)

[Ⅴ. Sequence Models] Natural Language Processing & Word Embeddings (3)

[Ⅴ. Sequence Models] Natural Language Processing & Word Embeddings (1)

[Ⅴ. Sequence Models] Recurrent Neural Networks (6)

[Ⅴ. Sequence Models] Recurrent Neural Networks (4)

[Ⅴ. Sequence Models] Recurrent Neural Networks (2)

[Ⅴ. Sequence Models] Recurrent Neural Networks (1)

[Ⅳ. Convolutional Neural Networks] Special Applications (1)

[Ⅳ. Convolutional Neural Networks] Object Detection (3)

[Ⅳ. Convolutional Neural Networks] Object Detection (2)

[Ⅳ. Convolutional Neural Networks] Deep Convolutional Models (6)

[Ⅳ. Convolutional Neural Networks] Deep Convolutional Models (5)

[Ⅳ. Convolutional Neural Networks] Deep Convolutional Models (4)

[Ⅳ. Convolutional Neural Networks] Deep Convolutional Models (3)

[Ⅳ. Convolutional Neural Networks] Convolutional Neural Networks (6)

[Ⅳ. Convolutional Neural Networks] Convolutional Neural Networks (5)

[Ⅳ. Convolutional Neural Networks] Convolutional Neural Networks (2)

[Ⅳ. Convolutional Neural Networks] Convolutional Neural Networks (1)

[Ⅲ. Structuring Machine Learning Projects] Learning from Multiple Tasks (2)

[Ⅲ. Structuring Machine Learning Projects] Learning from Multiple Tasks (1)

[Ⅲ. Structuring Machine Learning Projects] Comparing to Human-level Performance (3)

[Ⅲ. Structuring Machine Learning Projects] Comparing to Human-level Performance (2)

[Ⅲ. Structuring Machine Learning Projects] Comparing to Human-level Performance (1)

[Ⅱ. Deep Neural Network] Introduction to Programming Frameworks (1)

[Ⅱ. Deep Neural Network] Hyperparameter Tuning (2)

[Ⅱ. Deep Neural Network] Optimization Algorithms (6)

[Ⅱ. Deep Neural Network] Optimization Algorithms (3)

[Ⅱ. Deep Neural Network] Optimization Algorithms (2)

[Ⅱ. Deep Neural Network] Optimization Algorithms (1)

[Ⅱ. Deep Neural Network] Practical Aspects of Deep Learning (5)

[Ⅱ. Deep Neural Network] Practical Aspects of Deep Learning (4)

[Ⅱ. Deep Neural Network] Practical Aspects of Deep Learning (1)

[Ⅰ. Neural Networks and Deep Learning] Deep Neural Networks (3)

[Ⅰ. Neural Networks and Deep Learning] Deep Neural Networks (2)

[Ⅰ. Neural Networks and Deep Learning] Shallow Neural Networks (6)

[Ⅰ. Neural Networks and Deep Learning] Shallow Neural Networks (5)

[Ⅰ. Neural Networks and Deep Learning] Shallow Neural Networks (3)

[Ⅰ. Neural Networks and Deep Learning] Shallow Neural Networks (2)

[Ⅰ. Neural Networks and Deep Learning] Shallow Neural Networks (1)

[Ⅰ. Neural Networks and Deep Learning] Neural Networks Basics (7)

[Ⅰ. Neural Networks and Deep Learning] Neural Networks Basics (6)

[Ⅰ. Neural Networks and Deep Learning] Neural Networks Basics (4)

[Ⅰ. Neural Networks and Deep Learning] Neural Networks Basics (3)

[Ⅰ. Neural Networks and Deep Learning] Neural Networks Basics (2)

[Ⅰ. Neural Networks and Deep Learning] Introduction to Deep Learning (2)

[NLP] Conversation Model

[Machine Learning] SVM(Support Vector Machine) - Python

[Machine Learning] Linear Regression

[Machine Learning] SVM(Support Vector Machine) - 2. Margin

[NLP] Question Answering

[NLP] Machine Translation

[NLP] Text Classification

[NLP] Transformer

[NLP] NLP Research Trends (1)

[NLP] NLP Practices

[NLP] Word Representation (2)

[NLP] Word Representation (1)

[NLP] Natural Language Processing

[Kafka] 아파치 카프카(Apache Kafka)란 무엇인가? (2)

[Kafka] 아파치 카프카(Apache Kafka)란 무엇인가? (1)

[Database System] Concurrency Control (2)

Data Analytics vs Data Analysis

[Database System] Hashing File(2)

[Database System] Extenal Sorting

[Java] Stream API (5/5) - 연습문제 풀이

[Java] Stream API (3/5) - 활용 및 사용법 - 기초

[Java] Stream API (1/5) - Stream API에 대한 이해

[Database System] Tree Index (1)

[Database System] File Organizations(2)

[Database System] File Organizations(1)

[Database System] Relational Algebra

[Database System] Relational Model

[빅데이터분석기사 필기] Ⅳ.빅데이터 결과 해석 - 02. 분석 결과 해석 및 활용 (4)

[빅데이터분석기사 필기] Ⅳ.빅데이터 결과 해석 - 02. 분석 결과 해석 및 활용 (3)

[빅데이터분석기사 필기] Ⅳ.빅데이터 결과 해석 - 02. 분석 결과 해석 및 활용 (2)

[빅데이터분석기사 필기] Ⅳ.빅데이터 결과 해석 - 02. 분석 결과 해석 및 활용 (1)

[빅데이터분석기사 필기] Ⅲ.빅데이터 모델링 - 02. 분석기법 적용 (12) 비모수 통계

[빅데이터분석기사 필기] Ⅲ.빅데이터 모델링 - 02. 분석기법 적용 (11) 앙상블 분석

[빅데이터분석기사 필기] Ⅲ.빅데이터 모델링 - 02. 분석기법 적용 (10) 비정형 데이터 분석

[빅데이터분석기사 필기] Ⅲ.빅데이터 모델링 - 02. 분석기법 적용 (9) 립러닝 분석

[빅데이터분석기사 필기] Ⅲ.빅데이터 모델링 - 02. 분석기법 적용 (8) 베이지안기법

[빅데이터분석기사 필기] Ⅲ.빅데이터 모델링 - 02. 분석기법 적용 (7) 시계

[빅데이터분석기사 필기] Ⅲ.빅데이터 모델링 - 02. 분석기법 적용 (6) 범주형 자료 분석

[빅데이터분석기사 필기] Ⅱ.빅데이터 탐색 - 03. 통계기법 이해(3)

[빅데이터분석기사 필기] Ⅱ.빅데이터 탐색 - 03. 통계기법 이해(2)

[빅데이터분석기사 필기] Ⅱ.빅데이터 탐색 - 03. 통계기법 이해(1)

[빅데이터분석기사 필기] Ⅱ.빅데이터 탐색 - 02. 데이터 탐색(2)

[빅데이터분석기사 필기] Ⅱ.빅데이터 탐색 - 02. 데이터 탐색(1)

[빅데이터분석기사 필기] Ⅱ.빅데이터 탐색 - 01. 데이터 전처리 (4)

[빅데이터분석기사 필기] Ⅱ.빅데이터 탐색 - 01. 데이터 전처리 (3)

[빅데이터분석기사 필기] Ⅱ.빅데이터 탐색 - 01. 데이터 전처리 (2)

[빅데이터분석기사 필기] Ⅱ.빅데이터 탐색 - 01. 데이터 전처리 (1)