안녕하세요. 2017 인공지능 B팀 여러분!

 

8월 5일(토) 13주차 스터디는  [ 서울창조경제혁신센터 ]에서 진행하고자 합니다.

 

지난 스터디에 참석하지 못했으나 스터디에 참석을 희망하시는 분들은 아래에 댓글로 

남겨 주시기 바랍니다.

 

스터디 관련 의견사항이 있을 경우 댓글로 남겨주시면 감사하겠습니다.

 

 

주제:  

         - 동영상:

-- 인공지능과 기계학습_KMOOC

---  [  1.4-(Naive Bayes) ] 부터 

​- 도서 리뷰:

-- 밑바닥부터 시작하는 딥러닝

---  4장 신경망 학습 

 

장소: 

         - 서울창조경제혁신센터 열정 회의실 

            -- (5호선 광화문역 하차 → 2번출구 앞 서울창조경제혁신센터)

            -- (1호선 시청역 하차    → 4번출구 → 광화문역 방향 도보로 약 10분)

 

일시: 

         - 8월 5일(토요일)

         - 09:00 ~ 12:00

 

 

리뷰도서:

밑바닥부터 시작하는 딥러닝 - 파이썬으로 익히는 딥러닝 이론과 구현  사이토 고키 (지은이) | 개앞맵시 (옮긴이) | 한빛미디어 | 2017-01-03 | 원제 ゼロから作るDeep Learning (2016년) 『 밑바닥부터 시작하는 딥러닝 』(한빛미디어, 2017) | GitHub

 

관련 포스트 or 논문:

- The most comprehensive Data Science learning plan for 2017

- Convolutional neural networks, Part 1(post) | The Morning Paper

-- Convolutional neural networks, Part 2(post) | The Morning Paper

-- Convolutional neural networks, Part 3(post) | The Morning Paper

- Recurrent Neural Network models(post) | The Morning Paper

- RNN models for image generation_MARCH 3, 2017(post) | The Morning Paper

- Naive Bayes Classification

-- Naive Bayes Theorem(post)

-- Naive Bayes Classification explained with Python code(post)

-- How To Implement Naive Bayes From Scratch in Python(post)

-- 6 Easy Steps to Learn Naive Bayes Algorithm (with code in Python)(post)

-- A Programmer's Guide to Data Mining

--- Naïve Bayes and Probability Density Functions(post)

--- Python code for the free book A Programmer's Guide to Data Mining | GitHub

 

기타: 

- Numpy and Scipy Documentation

- Towards Machines that Perceive and Communicate

- 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 

- Hugo Larochelle

- (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

-- Schedule and Syllabus

- 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

-- Lecture Notes | Coursera

-- CS 229 Machine Learning Course Materials

-- Stanford Machine Learning

- 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)

-- DeepLearningBook 

-- 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 repository

-- 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 Mobile

-- TensorFlow Android Camera Demo

-- TensorFlow Raspberry Pi Examples

-- TensorFlow iOS Examples

 

 

 

 

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