[情報] DataScience的相關資源置底

看板DataScience作者st1009 (前端攻城師)時間6年前 (2018/03/22 22:06)推噓12(12推 0噓 8→)

留言20則, 12人參與討論串1/1

有鑑於置底空間有限，版主整理這篇文給大家參考索引歡迎大家發文或者推文提供資源和建議，版主不定期會更新這個列表 # --------------------------------------------------- #版友情報文作者:MLLAB [情報] ML resources #1Qcx4QMU (DataScience) 作者:aa155495 [情報] Mobile Deep learning Resource #1QcywWWC (DataScience) 作者:ruthertw [轉錄] 史上最完整機器學習自學攻略... #5QWh1oWM (DataScience) 作者:aaaba [轉錄] 基於TensorFlow的機器學習... #5Qb-tSvF (DataScience) 作者:RumiManiac [問題] 機器學習在動漫的應用 #1Q-Gv3pO (DataScience) 因為目前文章很少，直接將部份內容列出 # MLLAB [情報] ML resources #1Qcx4QMU (DataScience) 台大資工林軒田老師機器學習基石機器學習技法 https://www.csie.ntu.edu.tw/~htlin/mooc/ 台大電機李宏毅老師 machine learning (ML) machine learning and having it deep and structured (MLDS) http://speech.ee.ntu.edu.tw/~tlkagk/courses.html 台大電機李宏毅老師&台大資工陳縕儂老師 applied deep learning x machine learning and having it deep and structured (AD LxMLDS) https://www.csie.ntu.edu.tw/~yvchen/f106-adl/syllabus.html Stanford Andrew Ng Machine Learning https://www.coursera.org/learn/machine-learning University of Oxford Machine Learning https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/ David Silver RL course https://goo.gl/BGwF63 Stanford CNN for Visual Recognition http://cs231n.stanford.edu/syllabus.html Ian Goodfellow Deep Learning https://www.youtube.com/playlist?list=PLkISDyMVw2Htm42P0eTVEKyz7scxZ4V-O UIUC Dan Roth Machine Learning https://goo.gl/124noX 交大應數李育杰老師 Machine Learning http://ocw.nctu.edu.tw/course_detail-v.php?bgid=1&gid=1&nid=563 資料科學相關的課程從微積分、線代、機率、統計到機器學習都有 https://goo.gl/mKlq8r CS相關課程 https://www.ptt.cc/bbs/studyabroad/M.1511862466.A.D02.html AI、ML相關conference的deadline https://aideadlin.es/ Paper https://openreview.net/ https://arxiv.org/list/stat.ML/recent https://www.aaai.org/Library/conferences-library.php CV相關paper:http://openaccess.thecvf.com/menu.py GAN相關的paper:https://deephunt.in/the-gan-zoo-79597dc8c347 tensorflow相關資源 tutorials https://www.tensorflow.org/tutorials/ code範例 https://github.com/aymericdamien/TensorFlow-Examples 論壇 https://www.reddit.com/r/MachineLearning/ # aa155495 [情報] Mobile Deep learning Resource #1QcywWWC (DataScience) Survey paper A Survey of Model Compression and Acceleration for Deep Neural Networks [arXiv '17] https://arxiv.org/abs/1710.09282 -------------------------------------------------------- 輕量化 Model 1. MobilenetV2: Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation [arXiv '18, Google] https://arxiv.org/pdf/1801.04381.pdf 2. NasNet: Learning Transferable Architectures for Scalable Image Recognition [arXiv '17, Google] 註：Google AutoML 的論文 https://arxiv.org/pdf/1707.07012.pdf 3. DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices [AAAI'18, Samsung] https://arxiv.org/abs/1708.04728 4. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices [arXiv '17, Megvii] https://arxiv.org/abs/1707.01083 5. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications [arXiv '17, Google] https://arxiv.org/abs/1704.04861 6. CondenseNet: An Efficient DenseNet using Learned Group Convolutions [arXiv '17] https://arxiv.org/abs/1711.09224 ------------------------------------------------------------ System 1. DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications [MobiSys '17] https://www.sigmobile.org/mobisys/2017/accepted.php 2. DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware [MobiSys '17] http://fahim-kawsar.net/papers/Mathur.MobiSys2017-Camera.pdf 3. MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU [EMDL '17] https://arxiv.org/abs/1706.00878 4. DeepSense: A GPU-based deep convolutional neural network framework on commodity mobile devices [WearSys '16] http://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4278&context=sis_res earch 5. DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices [IPSN '16] http://niclane.org/pubs/deepx_ipsn.pdf 6. EIE: Efficient Inference Engine on Compressed Deep Neural Network [ISCA '16] https://arxiv.org/abs/1602.01528 7. MCDNN: An Approximation-Based Execution Framework for Deep Stream Processin g Under Resource Constraints [MobiSys '16] http://haneul.github.io/papers/mcdnn.pdf 8. DXTK: Enabling Resource-efficient Deep Learning on Mobile and Embedded Devices with the DeepX Toolkit [MobiCASE '16] 9. Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables [SenSys ’16] 10. An Early Resource Characterization of Deep Learning on Wearables, Smartpho nes and Internet-of-Things Devices [IoT-App ’15] 11. CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android [MM '16] 12. fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs [NIPS '17] -------------------------------------------------------------- Quantization (Model compression) 1. The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning [ICML'17] 2. Compressing Deep Convolutional Networks using Vector Quantization [arXiv'14] 3. Quantized Convolutional Neural Networks for Mobile Devices [CVPR '16] 4. Fixed-Point Performance Analysis of Recurrent Neural Networks [ICASSP'16] 5. Quantized Neural Networks: Training Neural Networks with Low Precision Weig hts and Activations [arXiv'16] 6. Loss-aware Binarization of Deep Networks [ICLR'17] 7. Towards the Limit of Network Quantization [ICLR'17] 8. Deep Learning with Low Precision by Half-wave Gaussian Quantization [CVPR'17] 9. ShiftCNN: Generalized Low-Precision Architecture for Inference of Convoluti onal Neural Networks [arXiv'17] 10. Training and Inference with Integers in Deep Neural Networks [ICLR'18] ------------------------------------------------------------ Pruning (Model Compression) 1. Learning both Weights and Connections for Efficient Neural Networks [NIPS'15] 2. Pruning Filters for Efficient ConvNets [ICLR'17] 3. Pruning Convolutional Neural Networks for Resource Efficient Inference [ICL R'17] 4. Soft Weight-Sharing for Neural Network Compression [ICLR'17] 5. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Qu antization and Huffman Coding [ICLR'16] 6. Dynamic Network Surgery for Efficient DNNs [NIPS'16] 7. Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning [CVPR'17] 8. ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression [ ICCV'17] 9. To prune, or not to prune: exploring the efficacy of pruning for model comp ression [ICLR'18] --------------------------------------------------------------- Approximation 1. Efficient and Accurate Approximations of Nonlinear Convolutional Networks [ CVPR'15] 2. Accelerating Very Deep Convolutional Networks for Classification and Detect ion (Extended version of above one) 3. Convolutional neural networks with low-rank regularization [arXiv'15] 4. Exploiting Linear Structure Within Convolutional Networks for Efficient Eva luation [NIPS'14] 5. Compression of Deep Convolutional Neural Networks for Fast and Low Power Mo bile Applications [ICLR'16] 6. High performance ultra-low-precision convolutions on mobile devices [NIPS'17] 其他版友推薦 1.Udacity 的免費DL課程由google的科學家親自講課 https://www.udacity.com/course/deep-learning--ud730 (全英授課對計畫留學的版友應該是不錯的資源) 2. 史丹佛大學的機器學習:自然語言處理的應用 https://www.youtube.com/watch?v=OQQ-W_63UgQ&list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7

EkRe6 3.Deep Learning(英文書) http://www.deeplearningbook.org/ 4.兩門修完林軒田老師的課後可以進修的課 https://goo.gl/HV39mG https://goo.gl/JK2esy -- * * * * 最美麗的詩歌是最絕望的詩歌 * * * * * * * * * * 有些不朽篇章是純粹的眼淚 * * * * * * * * * * * Alfred de Musset -- ※ 發信站: 批踢踢實業坊(ptt.cc), 來自: 1.163.147.50 ※ 文章網址: https://www.ptt.cc/bbs/DataScience/M.1521727605.A.4DF.html

推

vvind

03/23 16:12, 6年前 , 1^F

03/23 16:12, 1^F

推

TuCH

03/23 20:37, 6年前 , 2^F

03/23 20:37, 2^F

林軒田老師的教材好像頗受好評

推

a75468

03/24 01:14, 6年前 , 3^F