AI Repository
A curated list of resources about artificial intelligence (AI).
Maintained by Seonghan Ryu.
Inspired by the awsome lists.
Sharing
AI in General
Video Lectures
- Convolutional Neural Networks for Visual Recognition (Stanford), Fei-Fei Li et al., 2017 [Video] [Note]
- Learn TensorFlow and deep learning, without a Ph.D., Martin Görner, 2017
- Tensorflow for Deep Learning Research (Stanford), Chip Huyen, 2017
- 인공지능 및 기계학습 개론, 문일철, 2016
- Deep Learning Summer School, Montreal 2016, 2016
- Deep Learning by Google, Vincent Vanhoucke, 2016
- 모두를 위한 머신러닝 / 딥러닝, 김성훈, 2016
- C++로 배우는 Deep Learning, 홍정모, 2016
- Deep Learning (Oxford), Nando de Freitas, 2015
- Practical Deep Learning For Coders, Jeremy Howard, 2016
- Machine Learning, Andrew Ng, Coursera, 2014
- Neural Networks, Hugo Larochelle, 2013
Books
- Hands-On Machine Learning with Scikit-Learn and TensorFlow, Aurélien Géron, O’Reilly Media, Inc., 2017
- PRML (Korean ver.), Kiho Hong, Github, 2016
- TensorFlow For Machine Intelligence: A hands-on introduction to learning algorithms, Sam Abrahams et al., Bleeding Edge Press, 2016
- Deep Learning, Ian Goodfellow et al., MIT Press, 2016
- Neural Networks and Deep Learning, Michael Nielsen, Determination Press, 2015
- An Introduction to Statistical Learning, Gareth James et al., Springer, 2013
- The Elements of Statistical Learning, Trevor Hastie et al., Springer, 2009 [PDF]
Related Conferences & Workshops
- AAAI Conference on Artificial Intelligence (AAAI)
- Annual Conference on Neural Information Processing Systems (NIPS)
- International Conference on Machine Learning (ICML)
- International Joint Conference on Artificial Intelligence (IJCAI)
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- IEEE International Conference on Data Mining (ICDM)
- International Conference on Knowledge Discovery and Data Mining (KDD)
Tools
Reference
Deep Learning
- On the Origin of Deep Learning, Haohan Wang et al., arXiv, 2017
- UFLDL: Unsupervised Feature Learning and Deep Learning, Andrew Ng et al., 2013 [Wiki]
Data Science
- Team Data Science Process Documentation by Microsoft Azure
- The Data Science Process - A Visual Guide to Standard Procedures in Data Science
TensorFlow
- TensorFlow: A proposal of good practices for files, folders and models architecture, Morgan, 2017
- TensorFlow models
- Structuring Your TensorFlow Models, Danijar Hafner, 2016
Generative Adversarial Network
Reviews & Tutorials
- The GAN Zoo, Avinash Hindupur, 2017
- Generative Adversarial Nets in TensorFlow, Agustinus Kristiadi, Personal Blog, 2016
- Collection of Generative Models (GAN & VAE) in Pytorch and Tensorflow, Agustinus Kristiadi, Github, 2016
- How to Train a GAN? Tips and tricks to make GANs work, Soumith Chintala et al.
- 초짜 대학원생 입장에서 이해하는 GAN 시리즈, Jaejun Yoo, Personal Blog, 2017
- Generative Adversarial Networks, Ian Goodfellow, NIPS 2016 [Slide] [Paper]
Papers
- DualGAN: Unsupervised Dual Learning for Image-to-Image Translation, Zili Yi et al., 2017
- On the Effects of Batch and Weight Normalization in Generative Adversarial Networks, Sitao Xiang et al., 2017
- BEGAN: Boundary Equilibrium Generative Adversarial Networks, David Berthelot et al., 2017
- Wasserstein GAN, Martin Arjovsky et al., 2017 #WGAN
- Unrolled Generative Adversarial Networks, Luke Metz et al., 2016 [Code]
- Generative Adversarial Text to Image Synthesis, Scott Reed et al., ICML, 2016
- Learning Deep Representations of Fine-grained Visual Descriptions, Scott Reed et al., CVPR, 2016
- On distinguishability criteria for estimating generative models, Ian J. Goodfellow, ICLR Workshop, 2016
- Improved Techniques for Training GANs, Tim Salimans et al., arXiv, 2016
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Alec Radford et al., arXiv, 2016 #DCGAN
- InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets, Xi Chen et al., 2016 [Code]
- Conditional Generative Adversarial Nets, Mehdi Mirza et al., 2014
- Generative Adversarial Networks, Ian J. Goodfellow et al., 2014
Optimization
Reviews & Tutorials
- An overview of gradient descent optimization algorithms, Sebastian Ruder, Personal Blog, 2016
Papers
- Adam: a Method for Stochastic Optimization, Diederik P. Kingma et al., ICLR 2015
- ADADELTA: AN ADAPTIVE LEARNING RATE METHOD, Matthew D. Zeiler, arXiv, 2012
- Neural Networks for Machine Learning: Lecture 6a. Overview of mini-batch gradient descent, Geofre Hinton, Coursera, 2012 #RMSProp
- Random Search for Hyper-Parameter Optimization, James Bergstra et al., JMLR, 2012
Autoencoder
Reviews & Tutorials
- 초짜 대학원생의 입장에서 이해하는 Auto-Encoding Variational Bayes (VAE), Jaejun Yoo, Personal Blog, 2017
- Adversarial Autoencoders (with Pytorch), Felipe, Personal Blog, 2017
- Tutorial on Variational Autoencoders, Carl Doersch, 2016
Papers
- Adversarial Autoencoders, Alireza Makhzani et al., 2015 #AAE
- Denoising Criterion for Variational Auto-Encoding Framework, Daniel Jiwoong Im et al., 2015 #DAE
- Semi-Supervised Learning with Deep Generative Models, Diederik P. Kingma et al., NIPS, 2014
- Auto-Encoding Variational Bayes, Diederik P Kingma et al., 2013 #VAE
- Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Pascal Vincent et al., JMLR, 2010 #DAE
- Extracting and composing robust features with denoising autoencoders, Pascal Vincent et al., ICML, 2008 #DAE
- Greedy Layer-Wise Training of Deep Networks, Bengio et al., NIPS, 2006 #SAE
Initialization
- Understanding the difficulty of training deep feedforward neural networks, Xavier Glorot et al., AISTATS, 2010
Activation Function
- Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs), Djork-Arné Clevert et al., ICLR, 2016
Regularization & Normalization
- A Theoretically Grounded Application of Dropout in Recurrent Neural Networks, Yarin Gal et al., NIPS, 2016
- Batch Normalization 설명 및 구현, Beomsu Kim, 2016
- Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models, Sergey Ioffe et al., ICML, 2015
Recommendation System
- Practical Recommendations for Gradient-Based Training of Deep Architectures, Yoshua Bengio, 2012
Music
Game
- AlphaGo Pipeline 헤집기, Beomsu Kim, 2016
Reinforcement Learning
- Reinforcement Learning Summary, Kiho Hong, Personal Page, 2016
Model Compression
- BiQGEMM: Matrix Multiplication with Lookup Table For Binary-Coding-based Quantized DNNs - Korean Introduction, Jeon et al, Supercomputing, 2020
Uncategorized
- Machine Learning 스터디, Sanghyuk Chun et al., Perosnal Blog, 2014