틀:List:MachineLearning:Documentation

Bayesian probability

Bayesian Reasoning and Machine Learning: http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online; http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/020217.pdf; 020217_-_Bayesian_Reasoning_and_Machine_Learning.pdf

Deep learning

ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)

http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf

http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

ImageNet_Classification_with_Deep_Convolutional_Neural_Networks.pdf

[한글 번역] 깊은 컨볼루셔널 신경망을 이용한 이미지네트(ImageNet) 분류

: ImageNet_Classification_with_Deep_Convolutional_Neural_Networks_-_ko.pdf

Going deeper with convolutions (GoogleNet): http://arxiv.org/pdf/1409.4842v1.pdf; Going_deeper_with_convolutions.pdf; 번역: GoogleNet

특집원고 딥하이퍼넷 모델 (Deep Hypernetwork models) (서울대학교/장병탁): Deep_Hypernetwork_models_201508.pdf

[Mocrosoft] Deep Residual Learning for Image Recognition (Winner ILSVRC2015): Deep_Residual_Learning_for_Image_Recognition(Winner_ILSVRC2015)_Microsoft.pdf

[Microsoft] Fast R-CNN, Towards Real-Time Object Detection with Region Proposal Networks (Winner ILSVR2015): Fast_R-CNN,Towards_Real-Time_Object_Detection_with_Region_Proposal_Networks(Winner_ILSVR2015)_Microsoft.pdf

Learning Deconvolution Network for Semantic Segmentation: http://cvlab.postech.ac.kr/research/deconvnet/

Deep EXpectation of apparent age from a single image: https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks.: Inside_outside_net_detecting_objects_in_context_with_skip_pooling_and_recurrent_neural_networks_2015.pdf; Small Object 탐지방법에 관한 논문.

조대협의 블로그 - 수학포기자를 위한 딥러닝과 텐서플로우의 이해: http://bcho.tistory.com/1208; Machine_learning_ebooks_-_Machine_learning_for_those_who_abandon_math.pdf

Densely Connected Convolutional Networks (DenseNets): Deep learning CVPR2017 최고 논문상

Deep Learning Interviews book: Hundreds of fully solved job interview questions from a wide range of key topics in AI.

딥러닝 인터뷰 북

머신러닝을 배우는 석/박사 과정 및 구직자들을 위한 실전 질문과 솔루션 모음

인쇄본 구입도 가능하지만, 전체 PDF는 무료로 공개

https://arxiv.org/abs/2201.00650

[추천] NIA(한국지능정보사회진흥원)이 발간한 "2025 AI 동향과 이슈로 살펴보는 AI 시대에 꼭 알아야 할 핵심용어집": AI 시대에 꼭 알아야할 핵심 용어집 (168p PDF) | GeekNews; https://www.nia.or.kr/site/nia_kor/ex/bbs/View.do?cbIdx=82618&bcIdx=28928&parentSeq=28928; Nia.or.kr_-ai-_2025_terms.pdf

There Will Be a Scientific Theory of Deep Learning: 딥러닝에 대한 과학적 이론이 나올 것이다. | GeekNews; There Will Be a Scientific Theory of Deep Learning; 딥러닝의 학습 과정, 은닉 표현, 최종 가중치, 성능 등 주요 속성을 특성화하는 과학적 이론이 형성되고 있다는 주장을 담은 논문; 다섯 가지 연구 흐름(해석 가능한 이상화 설정, 다루기 쉬운 극한, 단순 수학 법칙, 하이퍼파라미터 이론, 보편적 행동)을 핵심 근거로 제시

Tutorials

딥러닝 제대로 시작하기 (지은이 오카타니 타카유키/옮긴이 심효섭): Deep_Learning_-Takayuki_Okatani-2015-_sample.pdf

머신러닝 입문 가이드 - IDG Deep Dive: http://www.itworld.co.kr/techlibrary/97428; IDG_DeepDive_Machine_learning-20160113.pdf

딥러닝의 이해 (미발간; 2016-08-22 ver): Understanding_deep_learning_0822.pdf

Fundamental of Reinforcement Learning: https://www.gitbook.com/book/dnddnjs/rl/details; Fundamental_of_Reinforcement_Learning.pdf; 모두의연구소 - 강화 학습의 기본

Deep Learning Papers Reading Roadmap (딥러닝 논문 로드맵): https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md; Deep_Learning_Papers_Reading_Roadmap.md.zip

[추천] Machine Learning-based Web Exception Detection (금융보안원 프로젝트 관련 참조사이트!): https://cloudfocus.aliyun.com/Machine-Learning-based-Web-Exception-Detection-89782?spm=a2c1b.a2c1b4.a2c1b4.16.ZSQoEd; Machine_Learning-based_Web_Exception_Detection_-Insights_and_Trends-_Alibaba_Cloud_Focus.pdf

머신러닝 기초 1~57편 (잡동사니 탐구 - 참스터디 ePaiai : 네이버 블로그): http://sams.epaiai.com/220498694383

Microsoft, ML for Beginners 강의 공개: MS Azure Clouds Advocates 팀이 만든 12주, 24강짜리 커리큘럼; Scikit-learn을 이용한 클래식 머신러닝 강의 (딥러닝은 별도 AI 강의로 나올 예정); https://github.com/microsoft/ML-For-Beginners

CS146S: The Modern Software Developer (한국어) | GeekNews: [원문] CS146S Korean Edition | The Modern Software Developer; 스탠포드 CS146S 강좌의 공식 한국어 버전

Compress model

Deep Compression and EIE - Deep Neural Network Model Compression and Efficient Inference Engine: Deep_compression_and_EIE_PPT.pdf

Learning bothWeights and Connections for Efficient Neural Networks: Learning_both_weights_and_connections_for_efficient_neural_networks_2015.pdf

Convolutional neural network

Reveal.js를 사용한 CNN 프레젠테이션 (Presentation).: Author - 나; Reveal-ml.tar.gz

Gradient-Based Learning Applied to Document Recognition: http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf; Gradient-Based_Learning_Applied_to_Document_Recognition.pdf

Visualizing and Understanding Convolutional Neural Networks: http://arxiv.org/abs/1311.2901; 1311.2901v3.pdf

Compressing CNN for Mobile Device (Samsung) - CNN 모델 압축의 필요성 etc ...: [http://mlcenter.postech.ac.kr/files/attach/workshop_fall_2015/삼성전자_김용덕_박사.pdf](http://mlcenter.postech.ac.kr/files/attach/workshop_fall_2015/삼성전자_김용덕_박사.pdf); Samsung_-_Compressing_CNN_for_Mobile_Device.pdf

Using Filter Banks in Convolutional Neural Networks for Texture Classification: https://arxiv.org/abs/1601.02919

Deep belief network

The Applications of Deep Learning on Traffic Identification: Us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification-wp.pdf; https://www.blackhat.com/docs/us-15/materials/us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification-wp.pdf

Deconvolution neural network

Learning Deconvolution Network for Semantic Segmentation: http://cvlab.postech.ac.kr/research/deconvnet/; https://arxiv.org/abs/1505.04366; 1505.04366.pdf

Segmentation

Learning to Segment (Facebook Research): https://research.fb.com/learning-to-segment/; DeepMask+SharpMask as well as MultiPathNet.

Recurrent Instance Segmentation: https://arxiv.org/abs/1511.08250; 1511.08250.pdf; Slideshare - Single Shot MultiBox Detector와 Recurrent Instance Segmentation

vid2vid: https://github.com/NVIDIA/vid2vid; Pytorch implementation for high-resolution (e.g., 2048x1024) photorealistic video-to-video translation.; It can be used for turning semantic label maps into photo-realistic videos, synthesizing people talking from edge maps, or generating human motions from poses.

Learning

Siamese Neural Networks for One-Shot Image Recognition: https://jayhey.github.io/deep%20learning/2018/02/06/saimese_network/; 딥러닝에서 네트워크를 학습시킬 때, 매우 많은 트레이닝 데이터가 필요합니다. 이러한 단점을 극복하여 한 레이블 당 하나의 이미지만 있어도 분류할 수 있게 학습시키는게 one-shot learning입니다.