I would continue adding papers to this roadmap. arXiv preprint arXiv:1603.08511 (2016). Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.” in CVPR, 2015. arXiv preprint arXiv:1502.05698(2015) [pdf] (bAbI tasks) ⭐⭐⭐,  Karl Moritz Hermann, et al. "A neural algorithm of artistic style." [pdf] (Milestone) ⭐⭐⭐⭐,  Koutník, Jan, et al. "Very deep convolutional networks for large-scale image recognition." [pdf] (Update of Batch Normalization) ⭐⭐⭐⭐,  Courbariaux, Matthieu, et al. arXiv preprint arXiv:1603.03417(2016). [pdf] (Google Speech Recognition System) ⭐⭐⭐,  Amodei, Dario, et al. Deep-Learning-Papers-Reading-Roadmap. arXiv preprint arXiv:1410.3916 (2014). Andrew Ng has often stated that the best approach (that he has seen) to mastering DL is to start reading papers and then to implement them. After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep learning can be applied to image and speech recognition issues. The roadmap is constructed in accordance with the following four guidelines: From outline to detail; From old to state-of-the-art ),  Szegedy, Christian, et al. DATA SCIENCE ROADMAP 2020. The roadmap is constructed in accordance with the following four guidelines: from outline to detail [pdf]⭐⭐⭐⭐,  Vinyals, Oriol, et al. [pdf] ⭐⭐⭐⭐,  Mordvintsev, Alexander; Olah, Christopher; Tyka, Mike (2015). [pdf] ⭐⭐⭐⭐,  He, Gkioxari, et al. "Low-shot visual object recognition." [pdf] ⭐⭐⭐⭐,  Gatys, Leon and Ecker, et al. arXiv preprint arXiv:1606.09549 (2016). [pdf] ⭐⭐⭐,  Levine, Sergey, et al. IEEE, 2013. rsingh2083/Deep-Learning-Papers-Reading-Roadmap. "Speech recognition with deep recurrent neural networks." "Deep speech 2: End-to-end speech recognition in english and mandarin." In this article, we list down 5 top deep learning research papers you must read. 4. "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups." "Instance-aware semantic segmentation via multi-task network cascades." arXiv preprint arXiv:1312.5602 (2013). [pdf] (SPPNet) ⭐⭐⭐⭐,  Girshick, Ross. The roadmap is constructed in accordance with the following four guidelines: From outline to detail; From old to state-of-the-art terryum/awesome-deep-learning-papers; floodsung/Deep-Learning-Papers-Reading-Roadmap; mhagiwara/100-nlp-papers; thunlp/GNNPapers; Content Understanding / Generalization / Transfer. "Human-level concept learning through probabilistic program induction." AI Expert Roadmap. [pdf] (Basic Prototype of Future Computer) ⭐⭐⭐⭐⭐,  Zaremba, Wojciech, and Ilya Sutskever. [pdf] (LSTM, very nice generating result, show the power of RNN) ⭐⭐⭐⭐,  Cho, Kyunghyun, et al. [pdf] ⭐⭐⭐⭐⭐,  L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. arXiv preprint arXiv:1511.06342 (2015). "Pointer networks." "Deep learning." Most of machine learning is built upon three pillars: linear algebra, calculus, and probability theory. AISTATS(2012) [pdf] ⭐⭐⭐⭐,  Mikolov, et al. "Sequence to sequence learning with neural networks." [pdf] ⭐⭐⭐⭐,  Yahya, Ali, et al. Here is a reading roadmap of Deep Learning papers! I would continue adding papers to this roadmap. Editor: What follows is a portion of the papers from this list. "Lifelong Machine Learning Systems: Beyond Learning Algorithms." Here is my roadmap of machine leanring and deep leanring materials. "Fast and accurate recurrent neural network acoustic models for speech recognition." "DRAW: A recurrent neural network for image generation." arXiv preprint arXiv:1608.07242 (2016). If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?". After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep learning can be applied to image and speech recognition issues. "Transferring rich feature hierarchies for robust visual tracking." Springer International Publishing, 2016. arXiv preprint arXiv:1207.0580 (2012). [pdf]⭐⭐⭐,  Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. "Going deeper with convolutions." Below you find a set of charts demonstrating the paths that you can take and the technologies that you would want to adopt in order to become a data scientist, machine learning or an ai expert. arXiv preprint arXiv:1602.07360 (2016). ANIPS(2014) [pdf] ⭐⭐⭐,  Ankit Kumar, et al. [pdf] (Baidu Speech Recognition System) ⭐⭐⭐⭐,  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:1512.02325 (2015). [pdf],  Karpathy, Andrej, and Li Fei-Fei. arXiv preprint arXiv:1503.02531 (2015). Proceedings of the IEEE International Conference on Computer Vision. they're used to log you in. "Deep speech 2: End-to-end speech recognition in english and mandarin." If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" "Fast and accurate recurrent neural network acoustic models for speech recognition." * https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap 14. Deep-Learning-Roadmap. [pdf] (Outstanding Work, A novel idea) ⭐⭐⭐⭐⭐,  Lake, Brenden M., Ruslan Salakhutdinov, and Joshua B. Tenenbaum. [pdf] ⭐⭐⭐⭐,  Ren, Shaoqing, et al. Proceedings of the IEEE International Conference on Computer Vision. European Conference on Computer Vision. [pdf] (PixelCNN) ⭐⭐⭐⭐,  Graves, Alex. Science 350.6266 (2015): 1332-1338. [pdf] ⭐⭐⭐,  Kulkarni, Girish, et al. In arXiv preprint arXiv:1411.4389 ,2014. Todayâs paper takes a look at what happened in Airbnb when they moved from standard machine learning approaches to deep learning. It is targeted towards beginners strapped for time, as â¦ "You only look once: Unified, real-time object detection." "Show, attend and tell: Neural image caption generation with visual attention". In arXiv preprint arXiv:1412.2306, 2014. You signed in with another tab or window. "Dueling network architectures for deep reinforcement learning." In arXiv preprint arXiv:1603.06147, 2016. In arXiv preprint arXiv:1502.03044, 2015. Letâs deep dive into each step and see what all ... Donât start reading maths book until and unless you are not in rush to ... Neural Network and Deep Learning. Introduction This post gives the track of my reading roadmap of papers. Advances in neural information processing systems. Advances in neural information processing systems. "One-shot Learning with Memory-Augmented Neural Networks." "Mastering the game of Go with deep neural networks and tree search." The roadmap is constructed in accordance with the following four guidelines: From outline to detail From old to state-of-the-art from generic to specific areas focus on state-of-the-art You will find many papers that are quite new but really worth reading. In arXiv preprint arXiv:1411.4952, 2014. If you are a newcomer to the Deep Learning area, the first question you may have is 'Which paper should I start reading from?' This post was written by Metis Senior Data Scientist Zachariah Miller, who is based in Chicago. "Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection." [pdf] (Modify previously trained network to reduce training epochs) ⭐⭐⭐,  Wei, Tao, et al. The Best Reinforcement Learning Papers from the ICLR 2020 Conference Posted May 6, 2020 Last week I had a pleasure to participate in the International Conference on Learning Representations ( ICLR ), an event dedicated to the research on all aspects of representation learning, commonly known as deep learning . "Towards End-To-End Speech Recognition with Recurrent Neural Networks." "Batch normalization: Accelerating deep network training by reducing internal covariate shift." arXiv.org. KDnuggets 20:n46, Dec 9: Why the Future of ETL Is Not ELT, ... Machine Learning: Cutting Edge Tech with Deep Roots in Other F... Top November Stories: Top Python Libraries for Data Science, D... 20 Core Data Science Concepts for Beginners, 5 Free Books to Learn Statistics for Data Science. 2015. Advances in Neural Information Processing Systems. "“Sequence to sequence learning with neural networks." [pdf] ⭐⭐⭐⭐,  Wang, Naiyan, and Dit-Yan Yeung. "Imagenet classification with deep convolutional neural networks." 14. Deep Learning Papers Reading Roadmap github.com. arXiv preprint arXiv:1610.04286 (2016). Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. The Best Deep Learning Papers from the ICLR 2020 Conference Posted May 5, 2020 Last week I had a pleasure to participate in the International Conference on Learning Representations ( ICLR ), an event dedicated to the research on all aspects of deep learning .
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