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Deep Learning Tutorials for 10 Weeks
License: MIT License
dl_tutorials_10weeks's Introduction
45 Papers + TF implementations
Alex Krizhevsky, et al. "ImageNet Classification with Deep Convolutional Neural Networks", NIPS, 2012
Christian Szegedy, et al. "Going Deeper with Convolutions", CVPR, 2015
Christian Szegedy, et al. "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning", ArXiv, 2016
Kaiming He, et al. "Deep Residual Learning for Image Recognition", CVPR, 2016
Andreas Veit, et al. "Residual Networks are Exponential Ensembles of Relatively Shallow Networks", ArXiv, 2016
Sergey Zagoruyko and Nikos Komodakis "Wide Residual Networks", ArXiv, 2016
Nitish Srivastava, et al. "Dropout- A Simple Way to Prevent Neural Networks from Overfitting", JMLR, 2014
Sergey Ioffe and Christian Szegedy "Batch Normalization- Accelerating Deep Network Training by Reducing Internal Covariate Shift, ArXiv, 2015
Algorithms behind AlphaGo
David Silver et al. "Mastering the game of Go with deep neural networks and tree search", Nature, 2016
Momentum, NAG, AdaGrad, AdaDelta, RMSprop, ADAM
Diederik Kingma and Jimmy Bam "ADAM: A Method For Stochastic Optimization", ICLR, 2015
Restricted Boltzmann Machine
Geoffrey Hinton, "A Practical Guide to Training Restricted Boltzmann Machines", 2010
Jonathan Long et al. "Fully Convolutional Networks for Semantic Segmentation", CVPR, 2015
Liang-Chieh Chen et al. "Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs", CVPR, 2015
Hyeonwoo Noh et al. "Learning Deconvolution Network for Semantic Segmentation", ICCV, 2015
Liang-Chieh Chen et al. "DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs", ArXiv, 2016
Weakly Supervised Localization
Maxime Oquab et al. "Is object localization for free? – Weakly-supervised learning with convolutional neural networks", CVPR, 2015
Bolei Zhou et al. "Learning Deep Features for Discriminative Localization", CVPR, 2016
Ross Girshick et al. "Rich feature hierarchies for accurate object detection and semantic segmentation", CVPR, 2014
Kaiming He et al. "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition", CVPR, 2015
Ross Girshick, "Fast R-CNN", ICCV, 2015
Shaoqing Ren et al. "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks", NIPS, 2015
Joseph Redmon et al. "You Only Look Once: Unified, Real-Time Object Detection", CVPR, 2016
Donggeun Yoo et al. "AttentionNet: Aggregating Weak Directions for Accurate Object Detection", ICCV, 2015
Wei Liu et al. "SSD: Single Shot MultiBox Detector", ECCV, 2016
Joseph Redmon, Ali Farhadi, "YOLO9000: Better, Faster, Stronger", ArXiv, 2017
Hyeonwoo Noh et al. "Image Question Answering using Convolutional Neural Network with Dynamic Parameter Prediction", CVPR, 2015
Akira Fukui et al. "Multimodal Compact Bilinear Pooling for VQA", CVPR, 2016
Deep reinforcement learning
Volodymyr Mnih et al. "Playing Atari with Deep Reinforcement Learning", NIPS, 2013
Hado van Hasselt et al. "Deep Reinforcement Learning with Double Q-learning", AAAI, 2016
Recurrent Neural Networks
Alex Graves, "Generating Sequences With Recurrent Neural Networks", ArXiv, 2013
Tomas Mikolov et al. "Distributed Representations of Words and Phrases and their Compositionality", NIPS, 2013
Oriol Vinyals et al. "Show and Tell: A Neural Image Caption Generator", CVPR, 2015
Kelvin Xu et al. "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention", ICML, 2015
Justin Johnson et al. "DenseCap: Fully Convolutional Localization Networks for Dense Captioning", CVPR, 2016
Leon A. Gatys et al. "Texture Synthesis Using Convolutional Neural Networks", NIPS, 2015
Aravindh Mahendran and Andrea Vedaldi, "Understanding Deep Image Representations by Inverting Them", CVPR, 2015
Leon A. Gatys et al. "A Neural Algorithm of Artistic Style", ArXiv, 2015
Generative adversarial networks
Ian J. Goodfellow et al. "Generative Adversarial Networks", NIPS, 2015
Alec Radford et al. "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", ICLR, 2016
Scott Reed et al. "Generative Adversarial Text to Image Synthesis", ICML, 2016
Donggeun Yoo et al. "Pixel Level Domain Transfer", ECCV, 2016
Phillip Isola et al, "Image-to-Image Translation with Conditional Adversarial Networks", ArXiv, 2016
Anh Nguyen et al. "Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space", ArXiv, 2016
Scott Reed et al. "Learning What and Where to Draw", NIPS, 2016
and implementations (which can be found in TF-101 )
Basic Python usage (numpy, matplotlib, ..)
Handling MNIST
Logistic regression
Multilayer Perceptron
Convolutional Neural Network
Denoising Autoencoders (+Convolutional)
Class Activation Map
Semantic Segmentation
Using Custom Dataset
Recurrent Neural Network
Char-RNN
Word2Vec
Neural Style
dl_tutorials_10weeks's People
Contributors
dl_tutorials_10weeks's Issues
Hi, @sjchoi86 ,
Thank you so much for organizing such a great materials on DL+TF. For the corresponding tensorflow code (listed under TensorFlow coding in syllabus.xlsx), where could we download them?
Thanks!
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