안녕하세요. 2017 인공지능 B팀 여러분!
7월 8일 9주차 스터디는 지난 스터디 시간에 협의한 장소에서 진행 할 예정입니다.
지난 스터디에 참석하지 못한 분들 중 이번 주 스터디에 참석을 희망하시는 분은
아래에 댓글로 남겨 주시기 바랍니다.
장소 및 스터디 관련 의견사항이 있을 경우 댓글로 남겨주시면 감사하겠습니다.
- 시즌 1 - 딥러닝의 기본 (TF 1.0 lab 업데이트중) 비디오 리스트
-- [ ML lab10: NN, ReLu, Xavier, Dropout, and Adam ] 부터
일시:
- 7월 08일(토요일)
- 오전 9시 ~ 오후 1시
예제코드:
- lab-11-5-mnist_cnn_ensemble_layers
추천도서:
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관련논문(CNN) & Post:
- Convolutional neural networks, Part 1(Post)
-- ImageNet classification with deep convolutional neural networks, Krizhevsky et al., 2012
-- Maxout networks, Goodfellow et al., 2013
-- Network in network, Lin et al., 2013
-- OverFeat: Integration recognition, localization and detection using convolutional networks, Sermanent et al., 2013
-- Identity Mappings in Deep Residual Networks (2016), K. He et al.
-- Deep residual learning for image recognition (2016), K. He et al.
-- Rethinking the inception architecture for computer vision (2016), C. Szegedy et al.
- An Intuitive Explanation of Convolutional Neural Networks(post) | Posted on August 11, 2016 by ujjwalkarn
기타:
- Tensorflow Tutorials using Jupyter Notebook | GitHub
-- TensorFlow Tutorial and Examples for beginners | GitHub , tensorflow.org
-- Simple tutorials using Google's TensorFlow Framework | GitHub
- CS 20SI: Tensorflow for Deep Learning Research
-- CS 20SI: Tensorflow for Deep Learning Research | YouTube
- TensorFlow Tutorial | YouTube
-- TensorFlow Tutorials | GitHub
-- Lecture 4 : Convolutional Neural Networks for Computer Vision
- Convolutional Networks | Deep Learning Summer School, Montreal 2015
- 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
-- Schedule and Syllabus(Winter 2016)
- CS224n: Natural Language Processing with Deep Learning
-- Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017) | YouTube
--- Lecture 13: Convolutional Neural Networks
- MIT 6.S094: Deep Learning for Self-Driving Cars | MIT
-- MIT 6.S094: Convolutional Neural Networks for End to End Learning of the Driving Task | YouTube
- Machine Learning | Coursera - Andrew Ng
-- Machine Learning(CS229) | YouTube — Andrew Ng, Stanford University [FULL COURSE]
-- Machine Learning(Coursera Contents) | YouTube — Andrew Ng, Stanford University
-- 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
- TensorBoard: Visualizing Learning
- Deep Learning and AI | Nahua Kang blog
-- Introducing Deep Learning and Neural Networks — Deep Learning for Rookies (1)
-- Multi-Layer Neural Networks with Sigmoid Function— Deep Learning for Rookies (2)
- 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 Summer School, Montreal 2016
-- Neural Networks - part - I & II -Hugo Larochelle
--- slide
- Deep Learning Book Review | YouTube
-- Deep Learning_An MIT Press book - Ian Goodfellow and Yoshua Bengio and Aaron Courville
-- Chapter 9 : Convolutional Networks
-- tensorflow/tensorflow/examples/tutorials/mnist/
- First Contact with TensorFlow(원서_번역내용) | 텐서플로우 블로그 (Tensor ≈ Blog)
-- Natural Language Processing with Deep Learning (Winter 2017) | YouTube
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