Artificial Intelligence
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Let’s study AI !
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TODO: New Reference
References
- awesome-machine-learning
- Machine Learning & Deep Learning Tutorials
- DeepLearning-Summary
- 머신러닝, 제대로 배우는 법
- Quora: What are the best tutorials, videos and slides for probabilistic graphical models?
- 수학을 포기한 직업 프로그래머가 머신러닝 학습을 시작하기위한 학습법 소개
- 쉽게 풀어쓴 딥러닝의 거의 모든것
- Tensor Flow Korea Facebook Top Articles
- 기계 학습(Machine Learning, 머신 러닝)은 즐겁다! Part 1
Math
- General
- Linear Algebra
- Matrix
- Coding the Matrix by Philip Klein
- Statistics
- Optimization
Famous People & Company
Short Articles or Video
- 쉽게 풀어쓴 딥러닝(Deep Learning)의 거의 모든 것 by 엄태웅님
- 기계 학습(Machine Learning, 머신 러닝)은 즐겁다!
- How Machine Learning is changing Software Development
- What makes TPUs fine-tuned for deep learning?
- Demis Hassabis, CEO, DeepMind Technologies - The Theory of Everything
- Yann LeCun - Accelerating Understanding: Deep Learning, Intelligent Applications, and GPUs
- Recent Advances in Deep Learning at Microsoft: A Selected Overview
- Lessons from My First Two Years of AI Research
Korean
Korean Lectures
- 휴먼 러닝 by 남세동
- KMOOC (2018) 인공지능 및 기계학습 개론Ⅰ
- PR12 딥럽닝 논문읽기 모임 Season1
- PR12 Season2
- 테리의 딥러닝 토크
- 인공지능 탐색과 최적화 기법 강좌
- 카이스트 문일철 교수 - 머신러닝 강좌
- 논문으로 시작하는 딥러닝
- 라온피플 머신러닝 아카데미
- DeepLearning-Summary
Korean Books
- Head First Data Analysis
- 통계의 힘
- R을 이용한 누구나 하는 통계분석
- R을 이용한 탐색적 자료 분석
- 집단 지성 프로그래밍
- 해커스타일로 배우는 기계학습
- 데이터 과학 입문
- 린분석
- 웹 데이터 분석학
- 월스트리트 저널 인포그래픽 가이드
- 번역자를 위한 우리말 공부
- 떨지마라 떨리게 하라
Tools
TensorFlow
- 모두를 위한 머신러닝/딥러닝
- https://www.inflearn.com/course/reinforcement-learning/
- https://wikidocs.net/book/587
- 골빈해커 텐서플로우 코딩 튜토리얼
- TensorFlow Examples Github
- Google 머신런닝 단기 집중과정(Tensorflow)
Python
- data-science-ipython-notebooks
- Python Machine Learning Mini-Course
- Scipy Lecture Notes
- Machine Learning with Python
- 파이썬 머신런닝 (책의 일부를 공개함)
- 파이썬 데이터 사이언스 Cheat Sheet: NumPy 기본
- Scikit-learn tutorial: statistical-learning for sientific data processing
- Python Data Science Handbook
Keras
R
ETC
- Hackers Guide to Neural Networks
- C++ 로 배우는 딥러닝
- Pytorch Zero to All
- ADVENTURES IN MACHINE LEARNINGLEARN AND EXPLORE MACHINE LEARNING
- https://github.com/aymericdamien/TensorFlow-Examples
- Automated Feature Engineering in Python
- Object Detection with 10 lines of code
Google Lectures
Microsoft Lectures
- Microsoft Professional Program for Artificial Intelligence
- Microsoft Professional Program for Data Science
- Microsoft Professional Program for Big Data
ETC Lectures
Lecture Sites
- https://www.kaggle.com/
- https://www.dataquest.io/
- https://www.datacamp.com/
- http://datamonkey.pro/
- Deeplearning.ai
Awsome Lists
Basic
Lectures
- Machine Learning (by Andrew Ng)
- CS156 - Learning From Data (by Yaser Abu-Mostafa)
- Machine Learning by GeorgiaTech
- Dive into Deep Learning
- OpenAI Spnning Up
- Intro to Machine Learning by UDACITY
Books
Advanced
Lectures
- Deep Learning Specialization. Master Deep Learning, and Break into AI by Andrew Ng
- Artificial Intelligence Nanodegree Programs by UDACITY
- fast.ai
- Machine Learning By Tom Mitchell
- CSED515/ITCE504 Machine Learning
- Reading Group: Pattern Recognition and Machine Learning
- Machine Learning (by Jeff Miller = Mathematicalmonk)
- Intro to Machine Learning, Pattern Recognition for Fun and Profit*
Books
- Pattern Recognition and Machine Learning (Information Science and Statistics) by Chistopher M. Bishop
- Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning) by Kevin Murphy
- NIPS & ICML tutorial0
- The Machine Learning Summer School
Papers & Conferences
- Awesome - Most Cited Deep Learning Papers
- arXiv.org
- CVPR
- NAACL
- NeurIPS
- ICLR
- ICML
- Two Minute Papers (Youtube)
Graphical Model
Lectures
- Probabilistic Graphical Model (by Daphne Koller)
- Machine Learning and Probabilistic Graphical Models (by Sargur Srihari)
- Probabilistic Graphical Model (by Andreas Karause from Caltech)
- Probabilistic Graphical Model (by Eric Xing from CMU)
- Probabilistic Graphical Model (by David Sontag from NYU)
Books
- An Introduction to Graphical Models (by Michael I. Jordan)
- Probabilistic Graphical Model: Principles and Techniques (by Daphne Koller)
Statistical Learning
Lectures
- Statistical Computing for Scientists and Engineers (University of Notre Dame)
- Statistics One offered by Andrew Conwa of Princeton University
Books
- The Elements of Statistical Learning
- An Introduction to Statistical Learning (with Applications in R)
- Statistical Learning with Sparsity
- The Probability and Statistics Cookbook
- Probabilistic Programming & Bayesian Methods for Hackers
Neural Network (Deep Learning)
Lectures
- Coursera (by Andrew Ng)
- Neural Networks class (by Hugo Larochelle)
- Neural Networks for Machine Learning (by Geoffrey Hinton)
- Theories of Deep Learning (STATS 385) (Stanford University)
- Deep Learning Specialization
Practical
- CS231n: Convolutional Neural Networks for Visual Recognition
- [CS224d: Deep Learning for Natural Language Processing by Ian Goodfellow]
Books
- Deep Learning (by Ian Goodfellow and Yoshua Bengio and Aaron Courville)
- Neural Networks and Deep Learning