Categories

Algorithm & Data Structure

• 디닉 알고리즘(Dinic's Algorithm) 11 Jul 2018
• 펜윅 트리(Fenwick Tree, Binary Indexed Tree, BIT) 09 Jul 2018
• greeksharifa's Library 07 Jul 2018
• 고속 푸리에 변환(Fast Fourier Theorem, FFT). 큰 수의 곱셈 07 Jul 2018
• 행렬의 N 거듭제곱 빠르게 구하기 04 Jul 2018
• Stack(스택) 29 Jun 2018

References

• seaborn 사용법(python seaborn 사용법) 13 May 2023
• matplotlib 사용법(python matplotlib.pyplot 사용법) 12 May 2023
• PyCharm 사용법 2 - 단축키 27 Apr 2023
• gspread 사용법(python gspread - google spreadsheet) 10 Apr 2023
• Python glob, os, platform, shutil 사용법(Python os 다루기) 07 Apr 2022
• MMDetection 사용법 2(Tutorial) 05 Sep 2021
• MMDetection 사용법 1(Quick Run) 30 Aug 2021
• Pytorch를 위한 Docker 사용법(Pytorch 도커 사용법) 21 Jun 2021
• Python time, datetime 사용법(Python 시간 다루기) 18 May 2021
• Linux(Ubuntu) terminal 명령어 정리 18 May 2021
• Python Selenium 사용법 [파이썬 셀레늄 사용법, 크롤링] 30 Oct 2020
• Weight & Biases(wandb) 사용법(wandb 설치 및 설명) 10 Jun 2020
• Python argparse 사용법 12 Feb 2019
• PyCharm 사용법(파이참 설치 및 사용법) 07 Feb 2019
• Miniconda(Anaconda) 사용법(conda 설치 및 사용법) 01 Feb 2019
• Jupyter Notebook 사용법(주피터 노트북 설치 및 사용법) 26 Jan 2019
• It will update soon... 13 Jul 2018
• TensorFlow 사용법 - 01. 소개 및 설치 10 Jul 2018
• Markdown 사용법 29 Jun 2018
• Github blog 수식 입력 방법 29 Jun 2018

GitHub

• GitHub 사용법 - 09. Overall(Git 명령어 정리, Git 사용법) 27 May 2020
• GitHub 사용법 - 08. Conflict 19 Aug 2018
• GitHub 사용법 - 07. diff, add, commit, .gitignore 중급 15 Aug 2018
• GitHub 사용법 - 06. branch 관리 12 Aug 2018
• GitHub 사용법 - 05. branch 기본 2 11 Aug 2018
• GitHub 사용법 - 04. branch 기본 1 07 Aug 2018
• GitHub 사용법 - 03. 프로젝트 clone, status check, .gitignore 08 Jul 2018
• GitHub 사용법 - 02. 프로젝트와 repository 생성 29 Jun 2018
• GitHub 사용법 - 01. 소개 29 Jun 2018
• GitHub 사용법 - 00. Command List 29 Jun 2018

PS

• BOJ 01000(A+B), 01001(A-B), 01002(터렛) 문제 풀이 16 Aug 2018
• BOJ 06086(최대 유량) 문제 풀이 12 Jul 2018
• BOJ 11658(구간 합 구하기 3) 문제 풀이 11 Jul 2018
• BOJ 01280(나무 심기) 문제 풀이 11 Jul 2018
• BOJ 02042(구간 합 구하기) 문제 풀이 10 Jul 2018
• BOJ 13277(큰 수 곱셈) 문제 풀이 08 Jul 2018
• BOJ 10828(스택) 문제 풀이 08 Jul 2018
• BOJ 09012(괄호) 문제 풀이 08 Jul 2018
• BOJ 06549(히스토그램에서 가장 큰 직사각형) 문제 풀이 07 Jul 2018

정규표현식(re)

• 파이썬 정규표현식(re) 사용법 - 09. 기타 기능 24 Aug 2018
• 파이썬 정규표현식(re) 사용법 - 08. 예제(단어, 행) 06 Aug 2018
• 파이썬 정규표현식(re) 사용법 - 07. 예제(숫자) 06 Aug 2018
• 파이썬 정규표현식(re) 사용법 - 06. 치환 함수, 양방탐색, 조건문 05 Aug 2018
• 파이썬 정규표현식(re) 사용법 - 05. 주석, 치환, 분리 04 Aug 2018
• 파이썬 정규표현식(re) 사용법 - 04. 그룹, 캡처 28 Jul 2018
• 파이썬 정규표현식(re) 사용법 - 03. OR, 반복 22 Jul 2018
• 파이썬 정규표현식(re) 사용법 - 02. 문자, 경계, flags 21 Jul 2018
• 파이썬 정규표현식(re) 사용법 - 01. Basic 20 Jul 2018

PyTorch

• Pytorch Geometric custom graph convolutional layer 생성하기 05 Sep 2021
• Pytorch Geometric Message Passing 설명 04 Sep 2021
• PyTorch 사용법 - 03. How to Use PyTorch 10 Nov 2018
• PyTorch 사용법 - 02. Linear Regression Model 02 Nov 2018
• PyTorch 사용법 - 01. 소개 및 설치 02 Nov 2018
• PyTorch 사용법 - 00. References 02 Nov 2018

Machine_Learning

• Tab-Transformer(Tabular Data Modeling using contextual embeddings) 설명 13 Mar 2022
• Strategies for pre-training Grapn Neural Networks 설명 01 Feb 2022
• Graphormer(Do Transformers Really Perform Bad for Graph Representation?) 설명 30 Jan 2022
• Bert4Rec(Sequential Recommendation with BERT) 설명 12 Dec 2021
• metapath2vec(Scalable Representation Learning for Heterogeneous Networks) 설명 11 Dec 2021
• Contrastive Learning, SimCLR 논문 설명(SimCLRv1, SimCLRv2) 09 Dec 2021
• Metric Learning 설명 06 Dec 2021
• BST(Behavior Sequence Transformer for E-commerce Recommendation in Alibaba) 설명 04 Dec 2021
• PCREC(Pre-training Graph Neural Network for Cross Domain Recommendation) 설명 28 Nov 2021
• Margin-based Loss 설명 24 Oct 2021
• HGT(Heterogeneous Graph Transformer) 설명 02 Oct 2021
• AB Test Sample Size 구하기 30 Sep 2021
• AB Test 기본 30 Sep 2021
• SIGN(Scalable Inception Graph Neural Networks) 설명 10 Sep 2021
• Graph Pooling - gPool, DiffPool, EigenPool 설명 09 Sep 2021
• GTN(Graph Transformer Networks) 설명 08 Sep 2021
• IGMC (Inductive Graph-based Matrix Completion) 설명 26 Aug 2021
• APPNP(Predict Then Propagate) 설명 20 Aug 2021
• ClusterGCN 설명 15 Aug 2021
• Graph Fourier Transform 설명 14 Aug 2021
• Graph Diffusion Convolution (GDC) 설명 12 Aug 2021
• Session-based Recommendation with GNN (SR-GNN) 설명 03 Jul 2021
• Graph Isomorphism Network (GIN) 설명 05 Jun 2021
• Graph Attention Networks (GAT) 설명 29 May 2021
• PinSAGE (Graph Convolutional Neural Networks for Web-Scale Recommender Systems) 설명 21 Feb 2021
• BLEURT - Learning Robust Metrics for Text Generation(BLEU 개선 버전, BLEURT 논문 설명) 13 Jan 2021
• GraphSAGE (Inductive Representation Learning on Large Graphs) 설명 31 Dec 2020
• Graph Convolutional Matrix Completion (GCMC) 설명 06 Dec 2020
• EM (Expected Maximization) 알고리즘 설명 28 Jul 2020
• Logistic Matrix Factorization 설명 02 Jun 2020
• LightFM 설명 01 Jun 2020
• Attentional Factorization Machines (AFM) 논문 리뷰 및 Tensorflow 구현 01 May 2020
• DeepFM 논문 리뷰 및 Tensorflow 구현 07 Apr 2020
• Field-aware Factorization Machines (FFM) 설명 및 xlearn 실습 05 Apr 2020
• Factorization Machines (FM) 설명 및 Tensorflow 구현 21 Dec 2019
• Matrix Factorization 설명 및 논분 리뷰 20 Dec 2019
• 잠재요인 협업필터링 (Latent Factor Collaborative Filtering) 설명 17 Dec 2019
• Light GBM 설명 및 사용법 09 Dec 2019
• Seaborn Module 사용법 05 Dec 2019
• Imbalanced Learning 01 Oct 2019
• Contextual Bandit and Tree Heuristic 18 Sep 2019
• 2019 ICML Papers(ICML 2019 논문 설명) 11 Jul 2019
• Bootstrap, Bagging, Boosting 06 Nov 2018

Generative Model

• Adversarial AutoEncoder (AAE) 설명 23 Aug 2020
• Conditional Variational AutoEncoder (CVAE) 설명 07 Aug 2020
• Variational AutoEncoder (VAE) 설명 31 Jul 2020
• Pix2Pix(Image-to-Image Translation with Conditional Adversarial Networks, Pix2Pix 논문 설명) 07 Apr 2019
• GAN의 개선 모델들(catGAN, Semi-supervised GAN, LSGAN, WGAN, WGAN_GP, DRAGAN, EBGAN, BEGAN, ACGAN, infoGAN), GAN의 개선 모델 설명 20 Mar 2019
• f-GAN(f-GAN 논문 설명) 19 Mar 2019
• CGAN(Conditional GAN), C-GAN 논문 설명 19 Mar 2019
• DCGAN(Deep Convolutional GAN, DCGAN 논문 설명) 17 Mar 2019
• GAN(Generative Adversarial Networks), GAN 논문 설명 03 Mar 2019

Computer Vision

• Learning to Retrieve Videos by Asking Questions 논문 설명 10 Aug 2022
• Resnet 계열 image classification 모델 설명 26 Jun 2022
• STAR benchmark 논문 설명(STAR - A Benchmark for Situated Reasoning in Real-World Videos) 07 Jun 2022
• EfficientNet 논문 설명(EfficientNet - Rethinking Model Scaling for Convolutional Neural Networks) 01 Mar 2022
• MobileNetV3 논문 설명(Searching for MobileNetV3 리뷰) 23 Feb 2022
• MobileNetV2 논문 설명(MobileNetsV2 - Inverted Residuals and Linear Bottlenecks 리뷰) 10 Feb 2022
• MobileNetV1 논문 설명(MobileNets - Efficient Convolutional Neural Networks for Mobile Vision Applications 리뷰) 01 Feb 2022
• StyleCLIP 논문 리뷰(StyleCLIP - Text-Driven Manipulation of StyleGAN Imagery) 24 Dec 2021
• CLIP 논문 리뷰(Learning Transferable Visual Models From Natural Language Supervision) 19 Dec 2021
• Video Swin Transformer 논문 설명 18 Dec 2021
• Swin Transformer V2 - Scaling Up Capacity and Resolution 논문 설명 15 Dec 2021
• Swin Transformer - Hierarchical Vision Transformer using Shifted Windows 논문 설명 14 Dec 2021
• VideoBERT - A Joint Model for Video and Language Representation Learning, CBT(Learning Video Representations using Contrastive Bidirectional Transformer) 논문 설명 13 Dec 2021
• VL-BERT, ViL-BERT 논문 설명(VL-BERT - Pre-training of Generic Visual-Linguistic Representations, ViLBERT - Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks) 12 Dec 2021
• ViT(Vision Transformer) 논문 설명(An Image is Worth 16x16 Words - Transformers for Image Recognition at Scale) 10 Dec 2021
• ViViT(Video ViT, ViViT - A Video Vision Transformer), MTN, TimeSFormer, MViT 논문 설명 10 Dec 2021
• Attention based Video Models 06 Dec 2021
• RNN based Video Models 04 Dec 2021
• Action Recognition Models(Two-stream, TSN, C3D, R3D, T3D, I3D, S3D, SlowFast, X3D) 04 Dec 2021
• CNN 기본 모델(ImageNet Challenge - AlexNet, ZFNet, VGG, GoogLeNet, ResNet, InceptionNet) 24 Oct 2021
• HowTo100M 설명(HowTo100M - Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips, Antoine Miech et al, 7 Jun 2019, 1906.03327) 18 Aug 2021
• HERO 논문 설명(HERO - Hierarchical Encoder for Video+Language Omni-representation Pre-training) 15 Aug 2021
• VATT 논문 설명(VATT - Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text) 04 Aug 2021
• KnowIT VQA - Answering Knowledge-Based Questions about Videos(KnowIT VQA 논문 설명) 24 Nov 2020
• KVQA - Knowledge-Aware Visual Question Answering(KVQA 논문 설명) 17 Nov 2020
• MovieQA(Movie Question Answering, MovieQA 논문 설명) 29 May 2019
• VQA(Visual Question Answering, VQA 논문 설명) 17 Apr 2019
• DANs(Dual Attention Networks for Multimodal Reasoning and Matching, DANs 논문 설명) 17 Apr 2019

NLP(Natural Language Processing) / RNNs

• BART 논문 설명(BART - Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension) 09 Aug 2022
• ERNIE 논문 설명(ERNIE 3.0 - Large-Scale Knowledge Enhanced Pre-Training For Language Understanding And Generation) 28 Jul 2021
• ERNIE 논문 설명(ERNIE-Doc - A Retrospective Long-Document Modeling Transformer) 25 Jul 2021
• ERNIE 논문 설명(ERNIE-ViL- Knowledge Enhanced Vision-Language Representations Through Scene Graph) 19 Jul 2021
• ERNIE 논문 설명(ERNIE 2.0 - A Continual Pre-Training Framework for Language Understanding) 05 Jul 2021
• ERNIE 논문 설명(ERNIE - Enhanced Language Representation with Informative Entities) 01 Jul 2021
• ERNIE 논문 설명(ERNIE - Enhanced Representation through Knowledge Integration) 14 Jun 2021
• OpenAI GPT-3 - Language Models are Few-Shot Learners(GPT3 논문 설명) 14 Aug 2020
• Explain Yourself! Leveraging Language Models for Commonsense Reasoning 08 Feb 2020
• OpenAI GPT-2 - Language Models are Unsupervised Multitask Learners(GPT2 논문 설명) 28 Aug 2019
• BERT - Pre-training of Deep Bidirectional Transformers for Language Understanding(BERT 논문 설명) 23 Aug 2019
• OpenAI GPT-1 - Improving Language Understanding by Generative Pre-Training(GPT1 논문 설명) 21 Aug 2019
• ELMo - Deep contextualized word representations(ELMo 논문 설명) 20 Aug 2019
• Attention Is All You Need(Attention 논문 설명) 17 Aug 2019
• Generating Sequences With Recurrent Neural Networks 15 Jul 2019

파이썬

• Python 프로젝트 생성하기 18 Jul 2022
• 파이썬 Error 처리 12 Jan 2020
• 파이썬 압축 모듈 간단 예시 11 Jan 2020
• 파이썬 collections, heapq 모듈 설명 10 Jan 2020
• 파이썬 numba 모듈 설명 16 Dec 2019
• 파이썬 logging Module 설명 13 Dec 2019

Bayesian_Statistics

• Monte Carlo Approximation (몬테카를로 근사 방법) 설명 30 Jul 2020
• Variational Inference (변분 추론) 설명 14 Jul 2020
• Gaussian Process 설명 12 Jul 2020

Self-Supervised Learning

• Self-Supervised Learning(자기지도 학습 설명) 01 Nov 2020

해외주식

• Hyperlocal SNS의 대표 주자, nextdoor 09 Aug 2021
• 중국 보험업의 선두 주자, 평안보험(Ping An of China) 23 Apr 2021
• 금리 상승기에 주목해야 하는 ETF, Vanguard Financials ETF (VFH) 04 Apr 2021

BlockChain

• LINK 코인(라인코인) 2021 계획 및 백서 요약 18 May 2021

MLOps

• Minikube 설치하기 12 Aug 2021
• Kubeflow 튜토리얼1 12 Aug 2021
• WSL2 Ubuntu 환경에서 Kubeflow 설치하기 05 Aug 2021

Apache Spark

• Apache Spark 기본 20 Sep 2021

SQL

• SQL 기본 01 Oct 2021

Discrete Representation

• VQ-VAE 2 논문 설명(Generating Diverse High-Fidelity Images with VQ-VAE-2) 26 Nov 2021
• VQ-VAE 논문 설명(Neural Discrete Representation Learning) 07 Nov 2021

Paper_Review

• AGQA 2.0 - An Updated Benchmark for Compositional Spatio-Temporal Reasoning 설명 06 Mar 2023
• AGQA - A Benchmark for Compositional Spatio-Temporal Reasoning 설명 03 Mar 2023
• LoRA - Low-Rank Adaptation of Large Language Models 요약 설명 21 Sep 2022
• GTN(Gated Transformer Networks for Multivariate Time Series Classification) 요약 설명 31 Aug 2022
• ScaleNorm - Transformers without Tears(Improving the Normalization of Self-Attention) 요약 설명 21 Aug 2022
• Recommendation for new users & items via randomized training and M-o-E transformation 요약 설명 19 Aug 2022
• PinnerSage(Multi-modal user embedding framework for recommendations at pinterest) 요약 설명 19 Aug 2022
• DropoutNet(Addressing Cold Start in Recommender Systems) 요약 설명 19 Aug 2022
• Linformer(Self-Attention with Linear Complexity) 요약 설명 18 Aug 2022
• Fastformer(Additive Attention Can Be All You Need) 요약 설명 18 Aug 2022
• Synthesizer(Rethinking self-attention for transformer models) 요약 설명 17 Aug 2022