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  • 深度学习路线图

    2017-10-18 11:47:00
    本资源为深度学习路线图给 初学者的照明灯 。
  • Deep-Learning-Papers-Reading-Roadmap(深度学习论文阅读路线图深度学习基础及历史 1.0 书 深度学习圣经:Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. “Deep learning.” An MIT Press book. ...

    Deep-Learning-Papers-Reading-Roadmap(深度学习论文阅读路线图)

    深度学习基础及历史

    1.0 书

    • 深度学习圣经:Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. “Deep learning.” An MIT Press book. (2015)

    1.1 报告

    • 三巨头报告:LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” Nature 521.7553 (2015)

    1.2 深度信念网络 (DBN)

    • 深度学习前夜的里程碑:Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. “A fast learning algorithm for deep belief nets.” Neural computation 18.7 (2006)
    • 展示深度学习前景的里程碑:Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. “Reducing the dimensionality of data with neural networks.” Science 313.5786 (2006)

    1.3 ImageNet革命(深度学习大爆炸)

    • AlexNet的深度学习突破:Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. “Imagenet classification with deep convolutional neural networks.” Advances in neural information processing systems. 2012.
    • VGGNet深度神经网络出现:Simonyan, Karen, and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition.” arXiv preprint arXiv:1409.1556 (2014).
    • GoogLeNet:Szegedy, Christian, et al. “Going deeper with convolutions.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
    • ResNet极深度神经网络,CVPR最佳论文:He, Kaiming, et al. “Deep residual learning for image recognition.” arXiv preprint arXiv:1512.03385 (2015).

    1.4 语音识别革命

    • 语音识别突破:Hinton, Geoffrey, et al. “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.” IEEE Signal Processing Magazine 29.6 (2012): 82-97.
    • RNN论文:Graves, Alex, Abdel-rahman Mohamed, and Geoffrey Hinton. “Speech recognition with deep recurrent neural networks.” 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013.
    • 端对端RNN语音识别:Graves, Alex, and Navdeep Jaitly. “Towards End-To-End Speech Recognition with Recurrent Neural Networks.” ICML. Vol. 14. 2014.
    • Google语音识别系统论文:Sak, Haşim, et al. “Fast and accurate recurrent neural network acoustic models for speech recognition.” arXiv preprint arXiv:1507.06947 (2015).
    • 百度语音识别系统论文:Amodei, Dario, et al. “Deep speech 2: End-to-end speech recognition in english and mandarin.” arXiv preprint arXiv:1512.02595 (2015).
    • 来自微软的当下最先进的语音识别论文:W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig “Achieving Human Parity in Conversational Speech Recognition.” arXiv preprint arXiv:1610.05256 (2016).

    深度学习方法

    2.1 模型

    • Dropout:Hinton, Geoffrey E., et al. “Improving neural networks by preventing co-adaptation of feature detectors.” arXiv preprint arXiv:1207.0580 (2012).
    • 过拟合:Srivastava, Nitish, et al. “Dropout: a simple way to prevent neural networks from overfitting.” Journal of Machine Learning Research 15.1 (2014): 1929-1958.
    • Batch归一化——2015年杰出成果:Ioffe, Sergey, and Christian Szegedy. “Batch normalization: Accelerating deep network training by reducing internal covariate shift.” arXiv preprint arXiv:1502.03167 (2015).
    • Batch归一化的升级:Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. “Layer normalization.” arXiv preprint arXiv:1607.06450 (2016).
    • 快速训练新模型:Courbariaux, Matthieu, et al. “Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to+ 1 or−1.”
    • 训练方法创新:Jaderberg, Max, et al. “Decoupled neural interfaces using synthetic gradients.” arXiv preprint arXiv:1608.05343 (2016).
    • 修改预训练网络以降低训练耗时:Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. “Net2net: Accelerating learning via knowledge transfer.” arXiv preprint arXiv:1511.05641 (2015).
    • 修改预训练网络以降低训练耗时:Wei, Tao, et al. “Network Morphism.” arXiv preprint arXiv:1603.01670 (2016).

    2.2 优化

    • 动量优化器:Sutskever, Ilya, et al. “On the importance of initialization and momentum in deep learning.” ICML (3) 28 (2013): 1139-1147.
    • 可能是当前使用最多的随机优化:Kingma, Diederik, and Jimmy Ba. “Adam: A method for stochastic optimization.” arXiv preprint arXiv:1412.6980 (2014).
    • 神经优化器:Andrychowicz, Marcin, et al. “Learning to learn by gradient descent by gradient descent.” arXiv preprint arXiv:1606.04474 (2016).
    • ICLR最佳论文,让神经网络运行更快的新方向:Han, Song, Huizi Mao, and William J. Dally. “Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding.” CoRR, abs/1510.00149 2 (2015).
    • 优化神经网络的另一个新方向:Iandola, Forrest N., et al. “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size.” arXiv preprint arXiv:1602.07360 (2016).

    2.3 无监督学习 / 深度生成式模型

    • Google Brain找猫的里程碑论文,吴恩达:Le, Quoc V. “Building high-level features using large scale unsupervised learning.” 2013 IEEE international conference on acoustics, speech and signal processing. IEEE, 2013.
    • 变分自编码机 (VAE):Kingma, Diederik P., and Max Welling. “Auto-encoding variational bayes.” arXiv preprint arXiv:1312.6114 (2013).
    • 生成式对抗网络 (GAN):Goodfellow, Ian, et al. “Generative adversarial nets.” Advances in Neural Information Processing Systems. 2014.
    • 解卷积生成式对抗网络 (DCGAN):Radford, Alec, Luke Metz, and Soumith Chintala. “Unsupervised representation learning with deep convolutional generative adversarial networks.” arXiv preprint arXiv:1511.06434 (2015).
    • Attention机制的变分自编码机:Gregor, Karol, et al. “DRAW: A recurrent neural network for image generation.” arXiv preprint arXiv:1502.04623 (2015).
    • PixelRNN:Oord, Aaron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. “Pixel recurrent neural networks.” arXiv preprint arXiv:1601.06759 (2016).
    • PixelCNN:Oord, Aaron van den, et al. “Conditional image generation with PixelCNN decoders.” arXiv preprint arXiv:1606.05328 (2016).

    2.4 RNN / 序列到序列模型

    • RNN的生成式序列,LSTM:Graves, Alex. “Generating sequences with recurrent neural networks.” arXiv preprint arXiv:1308.0850 (2013).
    • 第一份序列到序列论文:Cho, Kyunghyun, et al. “Learning phrase representations using RNN encoder-decoder for statistical machine translation.” arXiv preprint arXiv:1406.1078 (2014).
    • 神经机器翻译:Bahdanau, Dzmitry, KyungHyun Cho, and Yoshua Bengio. “Neural Machine Translation by Jointly Learning to Align and Translate.” arXiv preprint arXiv:1409.0473 (2014).
    • 序列到序列Chatbot:Vinyals, Oriol, and Quoc Le. “A neural conversational model.” arXiv preprint arXiv:1506.05869 (2015).

    2.5 神经网络图灵机

    • 未来计算机的基本原型:Graves, Alex, Greg Wayne, and Ivo Danihelka. “Neural turing machines.” arXiv preprint arXiv:1410.5401 (2014).
    • 强化学习神经图灵机:Zaremba, Wojciech, and Ilya Sutskever. “Reinforcement learning neural Turing machines.” arXiv preprint arXiv:1505.00521 362 (2015).
    • 记忆网络:Weston, Jason, Sumit Chopra, and Antoine Bordes. “Memory networks.” arXiv preprint arXiv:1410.3916 (2014).
    • 端对端记忆网络:Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. “End-to-end memory networks.” Advances in neural information processing systems. 2015.
    • 指针网络:Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. “Pointer networks.” Advances in Neural Information Processing Systems. 2015.

    2.6 深度强化学习

    • 第一篇以深度强化学习为名的论文:Mnih, Volodymyr, et al. “Playing atari with deep reinforcement learning.” arXiv preprint arXiv:1312.5602 (2013).
    • 里程碑:Mnih, Volodymyr, et al. “DeepMind:Human-level control through deep reinforcement learning.” Nature 518.7540 (2015): 529-533.
    • ICLR最佳论文:Wang, Ziyu, Nando de Freitas, and Marc Lanctot. “Dueling network architectures for deep reinforcement learning.” arXiv preprint arXiv:1511.06581 (2015).
    • 当前最先进的深度强化学习方法:Mnih, Volodymyr, et al. “Asynchronous methods for deep reinforcement learning.” arXiv preprint arXiv:1602.01783 (2016).
    • DDPG:Lillicrap, Timothy P., et al. “Continuous control with deep reinforcement learning.” arXiv preprint arXiv:1509.02971 (2015).
    • NAF:Gu, Shixiang, et al. “Continuous Deep Q-Learning with Model-based Acceleration.” arXiv preprint arXiv:1603.00748 (2016).
    • TRPO:Schulman, John, et al. “Trust region policy optimization.” CoRR, abs/1502.05477 (2015).
    • AlphaGo:Silver, David, et al. “Mastering the game of Go with deep neural networks and tree search.” Nature 529.7587 (2016): 484-489.

    2.7 深度迁移学习 / 终生学习 / 强化学习

    • Bengio教程:Bengio, Yoshua. “Deep Learning of Representations for Unsupervised and Transfer Learning.” ICML Unsupervised and Transfer Learning 27 (2012): 17-36.
    • 终生学习的简单讨论:Silver, Daniel L., Qiang Yang, and Lianghao Li. “Lifelong Machine Learning Systems: Beyond Learning Algorithms.” AAAI Spring Symposium: Lifelong Machine Learning. 2013.
    • Hinton、Jeff Dean大神研究:Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. “Distilling the knowledge in a neural network.” arXiv preprint arXiv:1503.02531 (2015).
    • 强化学习策略:Rusu, Andrei A., et al. “Policy distillation.” arXiv preprint arXiv:1511.06295 (2015).
    • 多任务深度迁移强化学习:Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. “Actor-mimic: Deep multitask and transfer reinforcement learning.” arXiv preprint arXiv:1511.06342 (2015).
    • 累进式神经网络:Rusu, Andrei A., et al. “Progressive neural networks.” arXiv preprint arXiv:1606.04671 (2016).

    2.8 一次性深度学习

    • 不涉及深度学习,但值得一读:Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. “Human-level concept learning through probabilistic program induction.” Science 350.6266 (2015): 1332-1338.
    • 一次性图像识别(暂无):Koch, Gregory, Richard Zemel, and Ruslan Salakhutdinov. “Siamese Neural Networks for One-shot Image Recognition.”(2015). pdf
    • 一次性学习基础(暂无):Santoro, Adam, et al. “One-shot Learning with Memory-Augmented Neural Networks.” arXiv preprint arXiv:1605.06065 (2016). pdf
    • 一次性学习网络:Vinyals, Oriol, et al. “Matching Networks for One Shot Learning.” arXiv preprint arXiv:1606.04080 (2016).
    • 大型数据(暂无):Hariharan, Bharath, and Ross Girshick. “Low-shot visual object recognition.” arXiv preprint arXiv:1606.02819 (2016). pdf

    应用

    3.1 自然语言处理 (NLP)

    • Antoine Bordes, et al. “Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing.” AISTATS(2012)
    • word2vec Mikolov, et al. “Distributed representations of words and phrases and their compositionality.” ANIPS(2013): 3111-3119
    • Sutskever, et al. “Sequence to sequence learning with neural networks.” ANIPS(2014)
      http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
    • Ankit Kumar, et al. “Ask Me Anything: Dynamic Memory Networks for Natural Language Processing.” arXiv preprint arXiv:1506.07285(2015)
    • Yoon Kim, et al. “Character-Aware Neural Language Models.” NIPS(2015) arXiv preprint arXiv:1508.06615(2015)
      https://arxiv.org/abs/1508.06615
    • bAbI任务:Jason Weston, et al. “Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks.” arXiv preprint arXiv:1502.05698(2015)
    • CNN / DailyMail 风格对比:Karl Moritz Hermann, et al. “Teaching Machines to Read and Comprehend.” arXiv preprint arXiv:1506.03340(2015)
    • 当前最先进的文本分类:Alexis Conneau, et al. “Very Deep Convolutional Networks for Natural Language Processing.” arXiv preprint arXiv:1606.01781(2016)
    • 稍次于最先进方案,但速度快很多:Armand Joulin, et al. “Bag of Tricks for Efficient Text Classification.” arXiv preprint arXiv:1607.01759(2016)

    3.2 目标检测

    • Szegedy, Christian, Alexander Toshev, and Dumitru Erhan. “Deep neural networks for object detection.” Advances in Neural Information Processing Systems. 2013.
    • RCNN:Girshick, Ross, et al. “Rich feature hierarchies for accurate object detection and semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
    • SPPNet(暂无):He, Kaiming, et al. “Spatial pyramid pooling in deep convolutional networks for visual recognition.” European Conference on Computer Vision. Springer International Publishing, 2014. pdf
    • Girshick, Ross. “Fast r-cnn.” Proceedings of the IEEE International Conference on Computer Vision. 2015.
    • 相当实用的YOLO项目:Redmon, Joseph, et al. “You only look once: Unified, real-time object detection.” arXiv preprint arXiv:1506.02640 (2015).
    • (暂无)Liu, Wei, et al. “SSD: Single Shot MultiBox Detector.” arXiv preprint arXiv:1512.02325 (2015). pdf
    • (暂无)Dai, Jifeng, et al. “R-FCN: Object Detection via Region-based Fully Convolutional Networks.” arXiv preprint arXiv:1605.06409 (2016). pdf
    • (暂无)He, Gkioxari, et al. “Mask R-CNN” arXiv preprint arXiv:1703.06870 (2017). pdf

    3.3 视觉追踪

    • 第一份采用深度学习的视觉追踪论文,DLT追踪器:Wang, Naiyan, and Dit-Yan Yeung. “Learning a deep compact image representation for visual tracking.” Advances in neural information processing systems. 2013.
    • SO-DLT(暂无):Wang, Naiyan, et al. “Transferring rich feature hierarchies for robust visual tracking.” arXiv preprint arXiv:1501.04587 (2015). pdf
    • FCNT:Wang, Lijun, et al. “Visual tracking with fully convolutional networks.” Proceedings of the IEEE International Conference on Computer Vision. 2015.
    • 跟深度学习一样快的非深度学习方法,GOTURN(暂无):Held, David, Sebastian Thrun, and Silvio Savarese. “Learning to Track at 100 FPS with Deep Regression Networks.” arXiv preprint arXiv:1604.01802 (2016). pdf
    • 新的最先进的实时目标追踪方案 SiameseFC(暂无):Bertinetto, Luca, et al. “Fully-Convolutional Siamese Networks for Object Tracking.” arXiv preprint arXiv:1606.09549 (2016). pdf
    • C-COT:Martin Danelljan, Andreas Robinson, Fahad Khan, Michael Felsberg. “Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking.” ECCV (2016)
    • VOT2016大赛冠军 TCNN(暂无):Nam, Hyeonseob, Mooyeol Baek, and Bohyung Han. “Modeling and Propagating CNNs in a Tree Structure for Visual Tracking.” arXiv preprint arXiv:1608.07242 (2016). pdf

    3.4 图像标注

    • Farhadi,Ali,etal. “Every picture tells a story: Generating sentences from images”. In Computer VisionECCV 201match0. Spmatchringer Berlin Heidelberg:15-29, 2010.
    • Kulkarni, Girish, et al. “Baby talk: Understanding and generating image descriptions”. In Proceedings of the 24th CVPR, 2011.
    • (暂无)Vinyals, Oriol, et al. “Show and tell: A neural image caption generator”. In arXiv preprint arXiv:1411.4555, 2014. pdf
    • RNN视觉识别与标注(暂无):Donahue, Jeff, et al. “Long-term recurrent convolutional networks for visual recognition and description”. In arXiv preprint arXiv:1411.4389 ,2014. pdf
    • 李飞飞及高徒Andrej Karpathy:Karpathy, Andrej, and Li Fei-Fei. “Deep visual-semantic alignments for generating image descriptions”. In arXiv preprint arXiv:1412.2306, 2014.
    • 李飞飞及高徒Andrej Karpathy(暂无):Karpathy, Andrej, Armand Joulin, and Fei Fei F. Li. “Deep fragment embeddings for bidirectional image sentence mapping”. In Advances in neural information processing systems, 2014. pdf
    • (暂无)Fang, Hao, et al. “From captions to visual concepts and back”. In arXiv preprint arXiv:1411.4952, 2014. pdf
    • (暂无)Chen, Xinlei, and C. Lawrence Zitnick. “Learning a recurrent visual representation for image caption generation”. In arXiv preprint arXiv:1411.5654, 2014. pdf
    • (暂无)Mao, Junhua, et al. “Deep captioning with multimodal recurrent neural networks (m-rnn)”. In arXiv preprint arXiv:1412.6632, 2014. pdf
    • (暂无)Xu, Kelvin, et al. “Show, attend and tell: Neural image caption generation with visual attention”. In arXiv preprint arXiv:1502.03044, 2015. pdf

    3.5 机器翻译

    • Luong, Minh-Thang, et al. “Addressing the rare word problem in neural machine translation.” arXiv preprint arXiv:1410.8206 (2014).
    • Sennrich, et al. “Neural Machine Translation of Rare Words with Subword Units”. In arXiv preprint arXiv:1508.07909, 2015.
    • Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. “Effective approaches to attention-based neural machine translation.” arXiv preprint arXiv:1508.04025 (2015).
    • Chung, et al. “A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation”. In arXiv preprint arXiv:1603.06147, 2016.
    • Lee, et al. “Fully Character-Level Neural Machine Translation without Explicit Segmentation”. In arXiv preprint arXiv:1610.03017, 2016.
    • Wu, Schuster, Chen, Le, et al. “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation”. In arXiv preprint arXiv:1609.08144v2, 2016.

    3.6 机器人

    • Koutník, Jan, et al. “Evolving large-scale neural networks for vision-based reinforcement learning.” Proceedings of the 15th annual conference on Genetic and evolutionary computation. ACM, 2013.
    • Levine, Sergey, et al. “End-to-end training of deep visuomotor policies.” Journal of Machine Learning Research 17.39 (2016): 1-40.
    • Pinto, Lerrel, and Abhinav Gupta. “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours.” arXiv preprint arXiv:1509.06825 (2015).
    • Levine, Sergey, et al. “Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection.” arXiv preprint arXiv:1603.02199 (2016).
    • Zhu, Yuke, et al. “Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning.” arXiv preprint arXiv:1609.05143 (2016).
    • Yahya, Ali, et al. “Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search.” arXiv preprint arXiv:1610.00673 (2016).
    • Gu, Shixiang, et al. “Deep Reinforcement Learning for Robotic Manipulation.” arXiv preprint arXiv:1610.00633 (2016).
    • A Rusu, M Vecerik, Thomas Rothörl, N Heess, R Pascanu, R Hadsell.”Sim-to-Real Robot Learning from Pixels with Progressive Nets.” arXiv preprint arXiv:1610.04286 (2016).
    • Mirowski, Piotr, et al. “Learning to navigate in complex environments.” arXiv preprint arXiv:1611.03673 (2016).

    3.7 艺术

    • Mordvintsev, Alexander; Olah, Christopher; Tyka, Mike (2015). “Inceptionism: Going Deeper into Neural Networks”. Google Research.
    • 当前最为成功的艺术风格迁移方案,Prisma:Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).
    • iGAN:Zhu, Jun-Yan, et al. “Generative Visual Manipulation on the Natural Image Manifold.” European Conference on Computer Vision. Springer International Publishing, 2016.
    • Neural Doodle:Champandard, Alex J. “Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks.” arXiv preprint arXiv:1603.01768 (2016).
    • Zhang, Richard, Phillip Isola, and Alexei A. Efros. “Colorful Image Colorization.” arXiv preprint arXiv:1603.08511 (2016).
    • 超分辨率,李飞飞:Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. “Perceptual losses for real-time style transfer and super-resolution.” arXiv preprint arXiv:1603.08155 (2016).
    • Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. “A learned representation for artistic style.” arXiv preprint arXiv:1610.07629 (2016).
    • 基于空间位置、色彩信息与空间尺度的风格迁移:Gatys, Leon and Ecker, et al.”Controlling Perceptual Factors in Neural Style Transfer.” arXiv preprint arXiv:1611.07865 (2016).
    • 纹理生成与风格迁移:Ulyanov, Dmitry and Lebedev, Vadim, et al. “Texture Networks: Feed-forward Synthesis of Textures and Stylized Images.” arXiv preprint arXiv:1603.03417(2016).

    3.8 目标分割

    • J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.” in CVPR, 2015.
    • L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. “Semantic image segmentation with deep convolutional nets and fully connected crfs.” In ICLR, 2015.
    • Pinheiro, P.O., Collobert, R., Dollar, P. “Learning to segment object candidates.” In: NIPS. 2015.
    • Dai, J., He, K., Sun, J. “Instance-aware semantic segmentation via multi-task network cascades.” in CVPR. 2016
    • Dai, J., He, K., Sun, J. “Instance-sensitive Fully Convolutional Networks.” arXiv preprint arXiv:1603.08678 (2016).

    其他

    4.0 补充

    • Big Data Mining.Deep Learning with Tensorflow(Google TensorFlow 深度学习)
    • Introduction to TensorFlow, Alejandro Solano - EuroPython 2017
    • Learning with TensorFlow, A Mathematical Approach to Advanced Artificial Intelligence in Python
    • Deep Learning with Python
    • Deep Learning with TensorFlow
    展开全文
  • 深度学习路线图参考

    千次阅读 2017-08-24 09:10:40
    1、机器学习/深度学习/自然...http://www.cnblogs.com/cyruszhu/p/5496913.html2、深度学习论文阅读路线图 Deep Learning Papers Reading Roadmap https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap

    1、机器学习/深度学习/自然语言处理学习路线
    http://www.cnblogs.com/cyruszhu/p/5496913.html

    2、深度学习论文阅读路线图 Deep Learning Papers Reading Roadmap
    https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap

    展开全文
  • 从学校到工作,学习了这么长时间,其实方向是很重要的,明确了方向,其次就是学习计划,要制定学习计划,那么学习路线是必不可少的。为此,我总结了深度学习路线图,该图是自己要学习的几个考量,仅供参考。 ...

    从学校到工作,学习了这么长时间,其实方向是很重要的,明确了方向,其次就是学习计划,要制定学习计划,那么学习路线是必不可少的。为此,我总结了深度学习的路线图,该图是自己要学习的几个考量,仅供参考。

    preview

    展开全文
  • 深度学习入门及深度学习学习路线

    万次阅读 多人点赞 2017-08-30 15:13:20
    前段时间无意间看到一些深度学习方面的资料,个人觉得写的实在是太精彩了,必须得推荐给他大家。目前只更新了7篇博客,里面包含了原理(即数学推导)和实践(代码实现),对于入门来讲实在是合适不过的了。 声明:...

    最近一段老师逼着搞论文,都没啥时间刷题和更新博客了。前段时间无意间看到一些深度学习方面的资料,个人觉得写的实在是太精彩了,必须得推荐给他大家。目前只更新了7篇博客,里面包含了原理(即数学推导)和实践(代码实现),对于入门来讲实在是合适不过的了。

    声明:本文只负责推荐,原文并非我写,尊重原创。

    在这放上原作者写的前言:

    下面给出每一部分的主题和详细链接。

    入门深度学习部分

    第一部分:感知机部分

    零基础入门深度学习-感知机


    第二部分:线性单元和梯度下降

    零基础入门深度学习-线性单元和梯度下降


    第三部分:神经网络和反向传播算法

    零基础入门深度学习-神经网络和反向传播算法


    第四部分:卷积神经网络

    零基础入门深度学习-卷积神经网络


    第五部分:循环神经网络

    零基础入门深度学习-循环神经网络


    第六部分:长短时记忆网络(LSTM)

    零基础入门深度学习-长短时记忆网络(LSTM)


    第七部分:递归神经网络

    零基础入门深度学习-递归神经网络


    看完这几篇文章之后,绝对有一种豁然开朗的感觉,确实写的非常精彩。理论和实践相结合的感觉绝逼是非常棒的,非常佩服原作者,写的非常的浅显易懂。欣赏完这几篇博客之后,估计大部分人都想进一步学习和了解深度学习,但是接着该咋走呢?我又整理了一篇文章来供大家参考(这也不是我写的,我只是推荐给大家而已)。

    深度学习论文学习路线(Deep Learning Papers Reading Roadmap)


    原作者写的前沿.路线图构建原则和相关的说明:

    具体学习路线:





    展开全文
  • 声明:感谢THU数据派公众号(datapi)授权发布。  原文:Deep Learning Papers Reading Roadmap  ...这里给出了深度学习论文阅读路线图路线图按照下面四个准则构建而成: 从提纲到细节从经典到前沿从通用领
  • 深度学习学习路径

    2020-03-10 16:13:13
    哈哈哈,我整理了一下深度学习学习路径
  • 入门深度学习部分 第一部分:感知机部分 零基础入门深度学习-感知机 第二部分:线性单元和梯度下降 零基础入门深度学习-线性单元和梯度下降 第三部分:神经网络和反向传播算法 零基础入门深度学习-神经网络和反向...
  • 深度学习技术路径

    千次阅读 2019-03-08 17:23:33
    主要从事计算机视觉技术和深度学习技术的研究与工业化应用,现担任人工智能初创公司中科视拓CEO。) 深度学习本质上是深层的人工神经网络,它不是一项孤立的技术,而是数学、统计机器学习、计算机科学和人工神经...
  • 深度学习图像分割学习路径深度学习图像分割学习路径深度学习图像分割学习路径 0 一些基础概念入门 一 传统图像知识 二 深度学习框架 pytorch tf2.0 三 深度学习–图像分类论文 0.Deep Learning 1.LeNet-1998 2....
  • 桔妹导读:滴滴的路线引擎每天要处理超过400亿次的路线规划请求,路径规划是滴滴地图输出的核心服务之一。不同于传统的路径规划算法,本文主要介绍的是一次深度强化学习路径规划业务场景下的探索...
  • 深度学习最佳学习路径

    千次阅读 2019-01-16 03:46:05
    深度学习最近为什么这么火外行所见的是2016年AlphaGo 4比1 战胜李世石,掀起了一波AI热潮,DeepMind背后所用的深度学习一时间火得不得了。其实在内行看来,AlphaGo对阵李世石的结果是毫无悬念的,真正的突破在几年前...
  • 基于深度强化学习路径规划笔记

    万次阅读 热门讨论 2019-04-24 20:57:31
    它输出一个由输入到输出的可能路径之一组成的图像。 下面显示的是程序的输入和输出。 输入图像被馈送到由2个conv和2个fc层组成的模型,其输出对应于底部和右侧动作的Q值。 代理根据哪个Q值更大而向右或向下移动,...
  • 声明:感谢THU数据派公众号(datapi)授权发布。 原文:Deep Learning Papers Reading ...这里给出了深度学习论文阅读路线图路线图按照下面四个准则构建而成: 从提纲到细节 从经典到前沿 从通用领域到特定领...
  • 着重掌握机器学习、深度学习、迁移学习。
  • 深度学习成长路线图

    千次阅读 2016-08-27 17:51:19
    1、阅读深度学习论文和教程,从介绍性的文字开始,逐渐提高难度。记录阅读心得,定期总结所学知识。2、把学到的算法自己实现一下,从零开始,保证你理解了其中的数学。别光照着论文里看到的伪代码复制一遍,实现一些...
  • 给研一同学们的深度学习学习规划

    万次阅读 多人点赞 2017-03-22 15:13:56
    学习路线 完成cs231n学习. 熟悉python,并完成对应assignment. 按照课程基础部分阅读相应基础paper. 按兴趣阅读特定应用方向paper. 学习某一种深度学习框架,并实现复现感兴趣方向一篇paper的实验结果. 总结汇报
  • 深度优先寻路算法是路径规划算法中的经典路径规划算法。

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