안녕하세요. 2017 인공지능 B팀 여러분!
8월 12일(토) 14주차 스터디는 [ 서울창조경제혁신센터 ]에서 진행하고자 합니다.
지난 스터디에 참석하지 못했으나 스터디에 참석을 희망하시는 분들은 아래에 댓글로
남겨 주시기 바랍니다.
스터디 관련 의견사항이 있을 경우 메시지를 남겨주시면 감사하겠습니다.
주제:
- 동영상:
--- [ 2.2-(Maximum Margin Classifier) ] 부터
- 도서 리뷰:
--- 5장 오차역전파법
- 아이디어 브레인 스토밍:
-- 머신러닝 & 딥러닝 이론을 기반으로 한 창의적 응용을 위한 아이디어 브레인 스토밍
--- 1인 1가지 아이디어 공유( 선택사항 )
장소:
- 서울창조경제혁신센터 꿈 회의실
-- (5호선 광화문역 하차 → 2번출구 앞 서울창조경제혁신센터)
-- (1호선 시청역 하차 → 4번출구 → 광화문역 방향 도보로 약 10분)
일시:
- 8월 12일(토요일)
- 09:00 ~ 12:00
리뷰도서:
|
『 밑바닥부터 시작하는 딥러닝 』(한빛미디어, 2017) | GitHub |
기타:
- Numpy and Scipy Documentation
- Towards Machines that Perceive and Communicate
- Machine Learning Course - CS 156 | Caltech
- ColumbiaX: CSMM.101x Artificial Intelligence (AI) | edx
-- 6.4 Naive Bayes - Bayes Rule
-- 6.4 Naive Bayes - Naive Bayes Classifier
- Convolutional Networks | Deep Learning Summer School, Montreal 2015
- (2017) 인공지능 및 기계학습 개론Ⅰ, 문일철 교수님 | edwith.org
-- KAIST 문일철 머신 러닝 강좌 | YouTube
--- (기계 학습, Machine Learning) Week 5 Support Vector Machine | Lecture 1 Decision boundary with Margin
- CS231n: Convolutional Neural Networks for Visual Recognition(Winter 2016)
-- CS231n Winter 2016 | YouTube
--- CS231n Winter 2016: Lecture 5: Neural Networks Part 2
--- CS231n Winter 2016: Lecture 6: Neural Networks Part 3 / Intro to ConvNets
--- CS231n Winter 2016: Lecture 7: Convolutional Neural Networks
--- CS231n Winter 2016: Lecture 10: Recurrent Neural Networks, Image Captioning, LSTM
-- Schedule and Syllabus(Winter 2016)
- CS224n: Natural Language Processing with Deep Learning
-- Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017) | YouTube
--- Lecture 8: Recurrent Neural Networks and Language Models
--- Lecture 13: Convolutional Neural Networks
- Machine Learning | Coursera - Andrew Ng
-- Machine Learning(CS229) | YouTube — Andrew Ng, Stanford University [FULL COURSE]
-- Machine Learning(Coursera Contents) | YouTube — Andrew Ng, Stanford University
--- 7.1.1 Support Vector Machines - Optimization Objective
-- CS 229 Machine Learning Course Materials
- Neural Networks for Machine Learning | Coursera - Prof. Geoffrey Hinton on Coursera in 2013
-- Neural Networks for Machine Learning | YouTube - Prof. Geoffrey Hinton on Coursera in 2012
-- CSC321 Winter 2014 - Lecture notes
- Learn TensorFlow and deep learning, without a Ph.D.
-- TensorFlow and Deep Learning without a PhD, Part 1 (Google Cloud Next '17)
-- TensorFlow and Deep Learning without a PhD, Part 2 (Google Cloud Next '17)
- Deep Learning Book Review | YouTube
-- Online Discussion: Deep Learning Book Ch 3
-- Online Discussion: Deep Learning Book Ch 9 (Part 1)
-- Online Discussion: Deep Learning Book Ch 9 (Part 2)
-- Online Discussion: Deep Learning Book Ch 10 (Part 1)
-- Online Discussion: Deep Learning Book Ch 10 (Part 2)
-- Deep Learning_An MIT Press book - Ian Goodfellow and Yoshua Bengio and Aaron Courville
--- Chapter 3: Probability and Information Theory
--- Chapter 9 : Convolutional Networks
--- Chapter 10 : Sequence Modeling: Recurrent and Recursive Nets
-- tensorflow/tensorflow/examples/tutorials/mnist/
- TensorFlow Machine Learning Cookbook_A Packt Publishing Book | GitHub
-- Ch 8: Convolutional Neural Networks | GitHub
-- Ch 9: Recurrent Neural Networks | GitHub
- TensorFlow Tutorial - used by Nvidia | GitHub
-- Lab2 - CNN | GitHub
-- Lab3 - RNN | GitHub
- TensorFlow Tutorial | YouTube
-- TensorFlow Tutorials | GitHub
-- TensorFlow Tutorial #02 Convolutional Neural Network | YouTube
-- TensorFlow Tutorial #02 Convolutional Neural Network | GitHub
-- TensorFlow Tutorial #03-B Layers API | YouTube
-- TensorFlow Tutorial #03-B Layers API | GitHub
-- TensorFlow Tutorial #05 - Ensemble Learning | YouTube
-- TensorFlow Tutorial #05 - Ensemble Learning | GitHub
-- TensorFlow Android Camera Demo
-- TensorFlow Raspberry Pi Examples
댓글 0
.