└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Deep-blogs 2 | Many well-curated lists of deep learning resources are available through repositories. They included course materials, papers and others. However, researchers' well-written blogs are lacked of information. I also study deep learning through online courses and papers, moreover blog materials have brought easily understanding of algorithms and implemenetations. In this repository, I especially listed blogs of researchers related to deep learning. I also recommend to go over open-project website: GitXiv. 3 | 4 | ## Online Courses 5 | * IPAM Summer School [[web]] 6 | (http://www.ipam.ucla.edu/programs/summer-schools/graduate-summer-school-deep-learning-feature-learning/?tab=schedule) 7 | * Popular researchers and professors in deep learning field teaches as a summer school. Although the materials are not the latest, tutorial contents are very beneficial. 8 | * IPAM Summer School: Tutorials on Theano/Torch [[web]] 9 | (https://github.com/clementfarabet/ipam-tutorials) 10 | * Opensource of deep learning frameworks are introduced. They are introduced at one of the programs in above summer school. 11 | * Deep Learning: Yann LeCun (2015-2016) [[web]] 12 | (https://www.college-de-france.fr/site/en-yann-lecun/course-2015-2016.htm) 13 | * Deep learning courses taught by Yann LeCun. English and French are provided. Lectures cover MLP, CNN, NLP, and unsupervised learning. 14 | * University of Toronto: CSC2541 - Differential Inference and Generative Models [[web]] 15 | (http://www.cs.toronto.edu/~duvenaud/courses/csc2541/index.html) 16 | * The latest course related to generative models is introduced. Unfortunately, lecture video is not provided. However, its selected papers and several lecture notes are very beneficial. I think trying to read papers shown this material is good start point to study generative models. 17 | * ELEG 5040: Advanced Topics in Signal Processing [[web]] 18 | (https://piazza.com/cuhk.edu.hk/spring2015/eleg5040/resources) 19 | * Resources related to deep learning are provided. 20 | 21 | ## Blogs 22 | * Andrej Karpathy: Hacker's guide to Neural Networks [[web]] 23 | (http://karpathy.github.io/neuralnets/) 24 | * This is one of the posts of Andrej Karpathy's. I think this should be in must-read list. 25 | * Brandon Amos: Ph.D students in CMU [[web]] 26 | (http://bamos.github.io/) 27 | * Blog post: \ was very beneficial. 28 | * Clément thorey: What does the gradient flowing through batch normalization looks like ? [[web]] 29 | (http://cthorey.github.io/backpropagation/) 30 | * Review and explanation of batch normalization 31 | * Two Sigma: A Survey of Selected Papers on Deep Learning at ICML 2016 [[web]] 32 | (https://www.twosigma.com/insights/a-survey-of-selected-papers-on-deep-learning-at-icml-2016) 33 | * To grasp latest research on deep learning, recent papers are needed to be read. To save time, these kinds of paper survey can be a reference. 34 | * Abubakar Abid: Introduction to Deep Learning in Medicine and Biology [[web]] 35 | (http://a12d.com/deep-learning-biomedicine) 36 | * Interesting post of using deep learning in medicine and biology fields. 37 | * Yarin Gal: University of Cambridge 38 | (http://mlg.eng.cam.ac.uk/yarin/blog.html) 39 | 40 | ### Computer Vision 41 | * Adit Deshpande: CS Undergrad at UCLA ('19) [[web]] 42 | (https://adeshpande3.github.io/adeshpande3.github.io/) 43 | * Introduction of the state-of-the-art ConvNets, GANS, and Reinforcement learnings are posted. 44 | * Aaditya Prakash: One by One [ 1 x 1 ] Convolution [[web]] 45 | (http://iamaaditya.github.io/2016/03/one-by-one-convolution/) 46 | * Why use 1x1 convolution in deep learning architecture is explained. 47 | * Arthur JulianiFollow: Cognitive Scientist & AI Researcher [[web]] 48 | (https://chatbotslife.com/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32#.n12cjesup) 49 | * State-of-the-art deep learning networks including ResNet and HighwayNets are posted with detailed explanation and implementation using TensorFlow. Other posts of this blog are also very beneficial. 50 | * Distill: Deconvolution and Checkerboard Artifacts [[web]] 51 | (http://distill.pub/2016/deconv-checkerboard/) 52 | * Great post of deconvolution comparison. The post is written by Google Brain researcher. I think this post is in must-read list. 53 | * Explained Visually: Image Kernels [[web]] 54 | (http://setosa.io/ev/image-kernels/) 55 | * Although deep learning is not explained in this post, included content of image kernels is beneficial. 56 | * Understanding Neural Networks Through Deep Visualization [[web]] 57 | (http://yosinski.com/deepvis) 58 | * This post explains about visualizing outputs of inner-layers in ConvNets. The paper is released from ICML. 59 | 60 | ### Unsupervised Learning 61 | * OpenAI: Generative Models [[web]] 62 | (https://openai.com/blog/generative-models/) 63 | * Nice explanation of generative model and its various versions. 64 | * Mind, etc.: Generating Faces with Deconvolution Networks [[web]] 65 | (https://zo7.github.io/blog/2016/09/25/generating-faces.html) 66 | * Generating faces is one of the applications of generative models. 67 | * Generative Adversarial Networks Explained with a Classic Spongebob Squarepants Episode [[web]] 68 | (https://medium.com/@awjuliani/generative-adversarial-networks-explained-with-a-classic-spongebob-squarepants-episode-54deab2fce39#.jiv4fnrno) 69 | * This post explains generative adversarial nets using Spongebob metaphor. It has a tutorial implementing GAN on Tensorflow. 70 | * Learning Interpretable Latent Representations with InfoGAN [[web]] 71 | (https://medium.com/emergent-future/learning-interpretable-latent-representations-with-infogan-dd710852db46#.8b1gr8jmc) 72 | * This post explains extension of GANs called InfoGAN. It has a tutorial implementing InfoGAN on Tensorflow. 73 | * Mark Swarbrick Jones: Generative adversarial autoencoders in Theano [[web]] 74 | (https://swarbrickjones.wordpress.com/2016/01/24/generative-adversarial-autoencoders-in-theano/) 75 | * Brief information of generative adversarial autoencoders 76 | * FAIR: The eyescream project [[web]] 77 | (http://soumith.ch/eyescream/) 78 | * Facebook genearting natural images project 79 | * Jan Hendrik Metzen: Variational Autoencoder in TensorFlow [[web]] 80 | (https://jmetzen.github.io/2015-11-27/vae.html) 81 | * Implementation of VAE using TensorFlow is explained step by step. 82 | * Fast Forward Labs: Under the Hood of the Variational Autoencoder [[web]] 83 | (http://blog.fastforwardlabs.com/post/149329060653/under-the-hood-of-the-variational-autoencoder-in) 84 | * Explanation and implementation of VAE using TensorFlow are posted by machine intelligence research company called Fast Forward Labs. 85 | * Introducing Neural Dream Videos [[web]] 86 | (https://medium.com/@awjuliani/introducing-neural-dream-videos-5d517b3cc804#.skva5r1zp) 87 | * This post introduce about neural deam videos, which is combined with variational autoencoder and recurrent neural network. Implementation of it is included as Github address. 88 | * What is variational autoencoders? [[web]] 89 | (https://jaan.io/what-is-variational-autoencoder-vae/) 90 | * This post explicitly explains about VAE. Excellent introduction to study VAE. 91 | * Introduction to Autoencoders [[web]] 92 | (https://pgaleone.eu/neural-networks/2016/11/18/introduction-to-autoencoders/) 93 | * Convolutional Autoencoders [[web]] 94 | (https://pgaleone.eu/neural-networks/2016/11/24/convolutional-autoencoders/) 95 | 96 | ### Natural Language Processing 97 | * Vered Shwartz: Probably Approximately a Scientific Blog [[web]] 98 | (http://veredshwartz.blogspot.kr/2016/01/representing-words.html) 99 | * This post explains methods of representing words which is mostly used in natural language processing. 100 | 101 | ### Reinforcement Learning 102 | * Ruben Fiszel's website: Reinforcement Learning and DQN, learning to play from pixels [[web]] 103 | (https://rubenfiszel.github.io/posts/rl4j/2016-08-24-Reinforcement-Learning-and-DQN.html) 104 | * Post of reinforcement learning and DQN with detailed explanation and implementation. 105 | 106 | 107 | 108 | ### Papers 109 | * Patrick Emami: Paper Summaries [[web]] 110 | (http://pemami4911.github.io/) 111 | * Summaries of papers related to machine learning and various topics concerning artificial intelligence are posted. 112 | 113 | 114 | ### Frameworks 115 | * Quantitative Journey: Beginner Tutorial: Neural Nets in Theano [[web]] 116 | (http://outlace.com/Beginner-Tutorial-Theano/) 117 | * Theano tutorial of neural nets 118 | * NVIDIA: Understanding Natural Language with Deep Neural Networks Using Torch [[web]] 119 | (https://devblogs.nvidia.com/parallelforall/understanding-natural-language-deep-neural-networks-using-torch/) 120 | * Explanation and implementation of natural language processing are posted. Torch is used. 121 | 122 | 123 | ## Others 124 | * Jeremy D. Jackson: Deep Learning [[web]] 125 | (http://www.jeremydjacksonphd.com/category/deep-learning/) 126 | * Curated list of deep learning resources. 127 | * Handong1587: Image Generation [[web]] 128 | (https://handong1587.github.io/deep_learning/2015/10/09/image-generation.html) 129 | * Curated list of image generation by Handong University. 130 | * Rodrigob: Classification datasets results [[web]] 131 | (http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html) 132 | * Current state of the art in object classification is collected. Well-known datasets: MNIST, CIFAR-10, CIFAR-100, STL-10, SVHN, ILSVRC2012 task 1 are shown. 133 | * SciRate: Computer Vision and Pattern Recognition (cs.CV) [[web]] 134 | (https://scirate.com/arxiv/cs.CV) 135 | * Lists of CVPR papers with abstracts. 136 | * Adam Harley: 2D Visualization of a Convolutional Neural Network [[web]] 137 | (http://scs.ryerson.ca/~aharley/vis/conv/flat.html) 138 | * Visual intuition of convolutional neural network 139 | * Reddit: Machine Learning [[web]] 140 | (https://www.reddit.com/r/MachineLearning/) 141 | * Machine learning channel in Reddit. 142 | --------------------------------------------------------------------------------