有监於置底空间有限，版主整理这篇文给大家参考索引 欢迎大家发文或者推文提供资源和建议，版主不定期会更新这个列表 # --------------------------------------------------- #版友情报文 作者:MLLAB [情报] ML resources #1Qcx4QMU (DataScience) 作者:aa155495 [情报] Mobile Deep learning Resource #1QcywWWC (DataScience) 作者:ruthertw [转录] 史上最完整机器学习自学攻略... #5QWh1oWM (DataScience) 作者:aaaba [转录] 基於TensorFlow的机器学习... #5Qb-tSvF (DataScience) 作者:RumiManiac [问题] 机器学习在动漫的应用 #1Q-Gv3pO (DataScience) 因为目前文章很少，直接将部份内容列出 # MLLAB [情报] ML resources #1Qcx4QMU (DataScience) 台大资工林轩田老师 机器学习基石 机器学习技法 https://www.csie.ntu.edu.tw/~htlin/mooc/ 台大电机李宏毅老师 machine learning (ML) machine learning and having it deep and structured (MLDS) http://speech.ee.ntu.edu.tw/~tlkagk/courses.html 台大电机李宏毅老师&台大资工陈縕侬老师 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 交大应数李育杰老师 Machine Learning http://ocw.nctu.edu.tw/course_detail-v.php?bgid=1&gid=1&nid=563 资料科学相关的课程 从 微积分、线代、机率、统计 到 机器学习 都有 https://goo.gl/mKlq8r CS相关课程 https://webptt.com/cn.aspx?n=bbs/studyabroad/M.1511862466.A.D02.html AI、ML相关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相关paper:http://openaccess.thecvf.com/menu.py GAN相关的paper:https://deephunt.in/the-gan-zoo-79597dc8c347 tensorflow相关资源 tutorials https://www.tensorflow.org/tutorials/ code范例 https://github.com/aymericdamien/TensorFlow-Examples 论坛 https://www.reddit.com/r/MachineLearning/ # aa155495 [情报] 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 -------------------------------------------------------- 轻量化 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] 注：Google AutoML 的论文 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] 其他版友推荐 1.Udacity 的免费DL课程 由google的科学家亲自讲课 https://www.udacity.com/course/deep-learning--ud730 (全英授课 对计画留学的版友 应该是不错的资源) 2. 史丹佛大学的 机器学习:自然语言处理的应用 https://www.youtube.com/watch?v=OQQ-W_63UgQ&list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7 EkRe6 3.Deep Learning(英文书) http://www.deeplearningbook.org/ 4.两门修完林轩田老师的课後可以进修的课 https://goo.gl/HV39mG https://goo.gl/JK2esy -- * * * * 最美丽的诗歌是最绝望的诗歌 * * * * * * * * * * 有些不朽篇章是纯粹的眼泪 * * * * * * * * * * * Alfred de Musset --

※ 发信站: 批踢踢实业坊(ptt.cc), 来自: 1.163.147.50 ※ 文章网址: https://webptt.com/cn.aspx?n=bbs/DataScience/M.1521727605.A.4DF.html 林轩田老师的教材好像颇受好评 已更新 已更新 ※ 编辑: st1009 (1.163.154.100), 05/16/2018 23:26:55