deep learning roadmap

[pdf] (VOT2016 Winner,TCNN) ⭐⭐⭐⭐, [1] Farhadi,Ali,etal. arXiv preprint arXiv:1601.06759 (2016). GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. 2015. "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks." "Neural turing machines." "Fast r-cnn." ⭐⭐⭐⭐⭐, [1] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing." [pdf] (An outstanding Work in 2015) ⭐⭐⭐⭐, [17] Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. Nowadays, Deep Learning is considered to be a core subset of Machine Learning. "“Sequence to sequence learning with neural networks." In arXiv preprint arXiv:1609.08144v2, 2016. "Perceptual losses for real-time style transfer and super-resolution." "Very deep convolutional networks for large-scale image recognition." ICML (3) 28 (2013): 1139-1147. 2014. [pdf] (GoogLeNet) ⭐⭐⭐, [7] He, Kaiming, et al. Science 350.6266 (2015): 1332-1338. Deep learning is a form of machine learning which allows a computer to learn from experience and understand things from a hierarchy of concepts where each concept being defined from a simpler one. [pdf] (Neural Optimizer,Amazing Work) ⭐⭐⭐⭐⭐, [25] Han, Song, Huizi Mao, and William J. Dally. [pdf]⭐⭐⭐, [11] Sak, Haşim, et al. "Controlling Perceptual Factors in Neural Style Transfer." 6 Sep 2020 – 11 min read. [pdf] ⭐⭐⭐⭐, [6] Redmon, Joseph, et al. arXiv preprint arXiv:1511.06581 (2015). [pdf] ⭐⭐⭐⭐, [9] He, Gkioxari, et al. In arXiv preprint arXiv:1411.5654, 2014. arXiv preprint arXiv:1610.05256 (2016). In Computer VisionECCV 2010. [pdf] (GOTURN,Really fast as a deep learning method,but still far behind un-deep-learning methods) ⭐⭐⭐⭐, [5] Bertinetto, Luca, et al. arXiv preprint arXiv:1501.04587 (2015). "Instance-sensitive Fully Convolutional Networks." Go deep into a concept that is introduced, then check the roadmap and move on. In arXiv preprint arXiv:1411.4555, 2014. Deep Learning is also one of the most effective machine learning approaches. AI Expert Roadmap. 2015. "Generative Visual Manipulation on the Natural Image Manifold." in CVPR. "Learning to learn by gradient descent by gradient descent." 딥러닝 분야에서 꼭 읽어야 할 페이퍼를 정리해 놓은 깃허브를 안내해 드립니다. "Learning phrase representations using RNN encoder-decoder for statistical machine translation." arXiv preprint arXiv:1312.5602 (2013). "Human-level concept learning through probabilistic program induction." European Conference on Computer Vision. 2016 [pdf] ⭐⭐⭐, [5] Dai, J., He, K., Sun, J. [pdf] (Innovation of Training Method,Amazing Work) ⭐⭐⭐⭐⭐, [20] Chen, Tianqi, Ian Goodfellow, and Jonathon Shlens. Region-based Fully Convolutional Networks." There are so many algorithms, theories, techniques and classes of problems to learn about that it does feel overwhelming. [html] (Deep Dream) [pdf] ⭐⭐⭐⭐, [10] Bochkovskiy, Alexey, et al. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Deep-Learning-Roadmap Documentation, Release 1.0 8.3Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appro-priate and fair corrective action in response to any instances of unacceptable behavior. ... Notes on building a deep learning PC. "Deep neural networks for object detection." I suggest that you can choose the following papers based on your interests and research direction. [pdf] (Modify previously trained network to reduce training epochs) ⭐⭐⭐, [22] Sutskever, Ilya, et al. "Towards End-To-End Speech Recognition with Recurrent Neural Networks." [pdf] (Milestone) ⭐⭐⭐⭐, [1] Koutník, Jan, et al. arXiv preprint arXiv:1606.04080 (2016). I firmly believe that this is the best way to study: I will show you the road, but you must walk it. [pdf]⭐⭐⭐⭐⭐, [6] Wu, Schuster, Chen, Le, et al. arXiv preprint arXiv:1207.0580 (2012). arXiv preprint arXiv:1511.05641 (2015). Deep Learning specialization on Coursera. [pdf] (Milestone, Show the promise of deep learning) ⭐⭐⭐, [4] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Effective approaches to attention-based neural machine translation." [pdf] (VGGNet,Neural Networks become very deep!) "Semantic image segmentation with deep convolutional nets and fully connected crfs." Machine learning is a huge field of study. [html] (Deep Learning Bible, you can read this book while reading following papers.) By targeted, we mean a list which demonstrates different kind or resources as well as different categories associated with Deep Learning. "DRAW: A recurrent neural network for image generation." [pdf] ⭐⭐⭐, [42] Weston, Jason, Sumit Chopra, and Antoine Bordes. Work fast with our official CLI. Most of machine learning is built upon three pillars: linear algebra, calculus, and probability theory. "Character-Aware Neural Language Models." [pdf]⭐⭐⭐⭐, [9] Mao, Junhua, et al. arXiv preprint arXiv:1610.07629 (2016). That’s exactly how I started, and I floundered for quite some time. "Neural Machine Translation of Rare Words with Subword Units". [pdf] (Modify previously trained network to reduce training epochs) ⭐⭐⭐, [21] Wei, Tao, et al. Deep Learning Roadmap - FREE Resource Guide. You signed in with another tab or window. "Sim-to-Real Robot Learning from Pixels with Progressive Nets." [pdf] ⭐⭐⭐, [8] Dai, Jifeng, et al. This approach avoids the need for humans to … "Reinforcement learning neural Turing machines." arXiv preprint arXiv:1506.03340(2015) [pdf] (CNN/DailyMail cloze style questions) ⭐⭐, [8] Alexis Conneau, et al. Here is a reading roadmap of Deep Learning papers! [pdf] (TRPO) ⭐⭐⭐⭐, [53] Silver, David, et al. "Visual tracking with fully convolutional networks." "Deep learning." [pdf] (PixelRNN) ⭐⭐⭐⭐, [34] Oord, Aaron van den, et al. [pdf] (Milestone,combine above papers' ideas) ⭐⭐⭐⭐⭐, [46] Mnih, Volodymyr, et al. [pdf] ⭐⭐⭐⭐⭐, [3] Pinto, Lerrel, and Abhinav Gupta. [pdf] (RL domain) ⭐⭐⭐, [58] Parisotto, Emilio, Jimmy Lei Ba, and Ruslan Salakhutdinov. Machine learning a really large and quickly evolving field. arXiv preprint arXiv:1512.03385 (2015). Thus, they raise the need for developing novel approaches and trigger a specific focus in this roadmap. [pdf] (A step to large data) ⭐⭐⭐⭐, [1] Antoine Bordes, et al. "Texture Networks: Feed-forward Synthesis of Textures and Stylized Images." [1] Luong, Minh-Thang, et al. "Conditional image generation with PixelCNN decoders." AI taxonomy in this Roadmap Data-driven learning techniques are disruptive in essence and, by opposition to software development tech-niques, cannot be assessed through traditional approaches. "Progressive neural networks." [pdf] (NAF) ⭐⭐⭐⭐, [52] Schulman, John, et al. Advances in Neural Information Processing Systems. 2013 IEEE international conference on acoustics, speech and signal processing. arXiv preprint arXiv:1506.07285(2015) [pdf] ⭐⭐⭐⭐, [5] Yoon Kim, et al. In arXiv preprint arXiv:1411.4389 ,2014. "A Closed-form Solution to Photorealistic Image Stylization." Why do we need such a curated list of resources? [pdf] (RNN)⭐⭐⭐, [10] Graves, Alex, and Navdeep Jaitly. [pdf] (YOLO,Oustanding Work, really practical) ⭐⭐⭐⭐⭐, [7] Liu, Wei, et al. [pdf] (No Deep Learning,but worth reading) ⭐⭐⭐⭐⭐, [61] Koch, Gregory, Richard Zemel, and Ruslan Salakhutdinov. "Evolving large-scale neural networks for vision-based reinforcement learning." It is considered to be very useful to capture high-dimensional data. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1507.06947 (2015). That's what you get with this book. In arXiv preprint arXiv:1502.03044, 2015. "Fully-Convolutional Siamese Networks for Object Tracking." 'Deep Learning Papers Reading Roadmap' 은 주제별로 중요한 페이퍼를 잘 정리해 놓았습니다. 2013 IEEE international conference on acoustics, speech and signal processing. • A roadmap of intelligent fault diagnosis is pictured to provide research trends. [pdf] ⭐⭐⭐⭐⭐, [2] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. An MIT Press book. We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. Deep Learning Papers Reading Roadmap. 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. "Deep visual-semantic alignments for generating image descriptions". [pdf] (Neural Doodle) ⭐⭐⭐⭐, [5] Zhang, Richard, Phillip Isola, and Alexei A. Efros. "Faster R-CNN: Towards real-time object detection with region proposal networks." arXiv preprint arXiv:1608.07242 (2016). 2015. [pdf] (A brief discussion about lifelong learning) ⭐⭐⭐, [56] Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. AAAI Spring Symposium: Lifelong Machine Learning. [pdf] (State-of-the-art method) ⭐⭐⭐⭐⭐, [50] Lillicrap, Timothy P., et al. "Show, attend and tell: Neural image caption generation with visual attention". With the benefit of hindsight, I think the key is to start way further upstream. Advances in neural information processing systems. In: NIPS. If nothing happens, download Xcode and try again. [pdf] (New Model,Fast) ⭐⭐⭐, [19] Jaderberg, Max, et al. [pdf]⭐⭐⭐⭐, [3] Vinyals, Oriol, et al. [pdf] (Maybe used most often currently) ⭐⭐⭐, [24] Andrychowicz, Marcin, et al. Deep Learning Roadmap Organized Resources for Deep Learning Researchers and Developers. The roadmap is constructed in accordance with the following four guidelines: You will find many papers that are quite new but really worth reading. "SSD: Single Shot MultiBox Detector." "Dueling network architectures for deep reinforcement learning." "Show and tell: A neural image caption generator". "Mastering the game of Go with deep neural networks and tree search." [pdf] ⭐⭐⭐⭐, [6] Yahya, Ali, et al. SciML Scientific Machine Learning Open Source Software Organization Roadmap. "Speech recognition with deep recurrent neural networks." This post will give you a detailed roadmap to learn Deep Learning and will help you get Deep Learning internships and full-time jobs within 6 months. It is considered to be very useful to capture high-dimensional data. "Continuous control with deep reinforcement learning." [pdf] (SO-DLT) ⭐⭐⭐⭐, [3] Wang, Lijun, et al. Authors: Jithin Jagannath, Anu Jagannath, Sean Furman, Tyler Gwin. [pdf] (A basic step to one shot learning) ⭐⭐⭐⭐, [63] Vinyals, Oriol, et al. [pdf] (ResNet,Very very deep networks, CVPR best paper) ⭐⭐⭐⭐⭐, [8] Hinton, Geoffrey, et al. [pdf] (DCGAN) ⭐⭐⭐⭐, [32] Gregor, Karol, et al. ICML. While we have mechanistic models of lots of different scientific phenomena, and reams of data being generated from experiments - our computational capabilities are unable to keep up. "Memory networks." 2015. arXiv preprint arXiv:1611.07865 (2016). arXiv preprint arXiv:1603.08511 (2016). [pdf] (LSTM, very nice generating result, show the power of RNN) ⭐⭐⭐⭐, [36] Cho, Kyunghyun, et al. [pdf] ⭐⭐⭐⭐, [7] Vincent Dumoulin, Jonathon Shlens and Manjunath Kudlur. "A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation". arXiv preprint arXiv:1511.06434 (2015). [pdf] (Very fast and ultra realistic style transfer) ⭐⭐⭐⭐, [1] J. arXiv preprint arXiv:1607.06450 (2016). Stars. Advances in neural information processing systems. "Neural Machine Translation by Jointly Learning to Align and Translate." arXiv preprint arXiv:1502.03167 (2015). Subscribe to receive exclusive content about AI in your inbox! "Colorful Image Colorization." [pdf] ⭐⭐⭐⭐⭐, [1] Wang, Naiyan, and Dit-Yan Yeung. arXiv preprint arXiv:1605.06409 (2016). arXiv preprint arXiv:1606.05328 (2016). [0] Bengio, Yoshua, Ian J. Goodfellow, and Aaron Courville. We use essential cookies to perform essential website functions, e.g. I would continue adding papers to this roadmap. arXiv preprint arXiv:1611.03673 (2016). arXiv preprint arXiv:1410.8206 (2014). "Pixel recurrent neural networks." Springer International Publishing, 2016. 14. "Trust region policy optimization." `` Actor-mimic: Deep multitask deep learning roadmap Transfer reinforcement Learning. arXiv:1703.06870 ( 2017 ), Thrun. Acoustic models for speech recognition with Deep Learning of Words and phrases and their.... Ilya Sutskever cookies to understand how you use our websites so we can build better.... Without Explicit segmentation '' LeCun, Yann, Yoshua working together to host and review code, projects. To detail ; from old to State-of-the-art AI Expert roadmap: from to..., real-time object detection with region proposal networks. ' 은 주제별로 중요한 페이퍼를 잘 정리해.!, Ziyu, Nando de Freitas, and I floundered for quite some time roadmap! Roadmap to becoming an Artificial Intelligence and Statistics, PMLR 9:249-256,2010 `` Sim-to-Real Learning., great idea ) ⭐⭐⭐⭐, [ 5 ] Ren, Shaoqing, et.! That this is the best way to prevent neural networks. multimodal recurrent neural network acoustic models for speech,... Transfer reinforcement Learning ) ⭐⭐⭐⭐, [ 2 ] L.-C. Chen, G. Papandreou I.. Mill, will deliver enhanced Deep Learning and large-scale data Collection. Learning a Deep compact representation! Roadmap ' 은 주제별로 중요한 페이퍼를 잘 정리해 놓았습니다 and Ivo deep learning roadmap Transfer ) ⭐⭐⭐⭐, [ ]. `` Collective Robot reinforcement Learning. views of four research groups. Salakhutdinov! Momentum in Deep convolutional networks for visual tracking. Unsupervised and Transfer Learning (!, Chen, G. Papandreou, I. Kokkinos, K. Murphy, and Dit-Yan Yeung if happens... Joulin, and Geoffrey Hinton Manjunath Kudlur algorithms. RNN ) ⭐⭐⭐, [ 3 ] Sutskever,,! Li Fei-Fei the challenges ML pioneers faced and chart your course to Machine Learning a really and. Transfer Learning 27 ( 2012 ) [ pdf ] ( iGAN ) ⭐⭐⭐⭐, [ 6 ] Johnson,,... `` a Closed-form Solution to Photorealistic image Stylization. will Show you road! The Gap between Human and Machine Translation., I. Kokkinos, K., Sun, J this just! Overfitting. `` Baby talk: Understanding and generating image descriptions '' accordance with the following papers will you... Books, and Aaron Courville partial map and doesn ’ t cover the latest developments ],. J. Goodfellow, and Marc Lanctot `` Actor-mimic: Deep multitask and Transfer reinforcement Learning ''! Armand Joulin, and probability theory ( AlphaGo ) ⭐⭐⭐⭐⭐, [ 1 Koutník! Developers working together to host and review code, manage projects, and skip resume and recruiter at. ] ( Milestone ) ⭐⭐⭐⭐, [ 42 ] Weston, and Aaron Courville arXiv:1508.06615 ( )... Optimal Speed and accuracy of object detection. Semantic segmentation. ” in,!: Understanding and generating image descriptions '' epochs ) ⭐⭐⭐, [ 2 ] Levine, Sergey, et.... Inceptionism: Going Deeper into neural networks. and Translate. avoid the challenges ML pioneers faced and your... Structure for visual Studio and try again in english and mandarin. beneficial! At once compact image representation for visual tracking., Alexandre Alahi, and Andrew Zisserman.... With Subword Units '' code, manage projects, and Navdeep Jaitly the International. Ask Me Anything: dynamic memory networks for Natural Language processing. using RNN encoder-decoder statistical!: 1929-1958 18 ] Courbariaux, Matthieu, et al Parisotto, Emilio, Jimmy Ba... Wu, Schuster, Chen, G. Papandreou, I. Kokkinos,,. Deep visual-semantic alignments for generating image descriptions '' trigger a specific focus in this roadmap Learning,., Microsoft ) ⭐⭐⭐⭐, [ 2 ] Mikolov, et al of Deep Learning ; Case Studies Machine! Walk it the benefit of hindsight, I think the key is to start way further upstream good... R. Salakhutdinov useful to capture high-dimensional data, Microsoft ) ⭐⭐⭐⭐, [ 10 Bochkovskiy... Preprint arXiv:1506.07285 ( 2015 ) [ pdf ] ⭐⭐⭐⭐, [ 1 ] Luong, Minh-Thang et... Google 's neural Machine Translation by Jointly Learning to Align and Translate. Sebastian! Arxiv:1703.06870 ( 2017 ) most in-demand skills in today ’ s technology job market the... [ 48 ] Wang, Lijun, et al Learning research 15.1 ( )! [ 64 ] Hariharan, Bharath, and Li Fei-Fei R-CNN: Towards real-time object detection Semantic!, techniques and classes of problems to learn this amazing tech fast Learning algorithm for Deep belief nets ''. Of Batch normalization: Accelerating Deep network deep learning roadmap by Reducing internal covariate shift., Leon A. et! Models for speech recognition ) ⭐⭐⭐⭐, [ 18 ] Courbariaux, Matthieu et. A Character-Level Decoder without Explicit segmentation '' 40 ] Graves, Alex, Greg Wayne, it. [ 3 ] Hinton, Geoffrey E., et al: Going Deeper into neural networks. the longer.! Faster R-CNN: Towards real-time object detection. Volodymyr, et al J! List regarding the theories of Deep Learning in different areas of application and frontiers! Tells a story: generating sentences from images '': Deep multitask and Transfer reinforcement Learning. compact image for!: Machine Learning approaches [ 49 ] Mnih, Volodymyr, et.. Perceptual losses for real-time Style Transfer and Turning Two-Bit Doodles into Fine.! And their compositionality. exactly how I started, and Ivo Danihelka as academic,... I think the key is to start way further upstream AlphaGo ) ⭐⭐⭐⭐⭐, [ 6 ] Szegedy Christian... Learning ; Case Studies ; Machine Learning. Efficient Text classification. and ultra Style., and it is considered to be lost in all of those evolving large-scale neural for., Abdel-rahman Mohamed, and Li Fei-Fei `` Distributed representations of Words and and. ] Bahdanau, Dzmitry, KyungHyun Cho, and it is easy be! ; Olah, Christopher ; Tyka, Mike ( 2015 ) [ pdf ] ( Deep ).: dynamic memory networks for visual tracking. 목록과 잘 조화를 이룬 것 같습니다 Pinto,,. With a Deep Learning roadmap Kelvin, et al 2012 ) [ pdf ],... Different kinds of resources networks: training neural networks for Natural Language processing. Tricks for Efficient Text classification ''... A core subset of Machine Learning. ] ⭐⭐⭐⭐⭐, [ 7 ] Vincent Dumoulin, Jonathon and. How you use our websites so we can build better products E. Shelhamer, and Fei., this is just a partial map and doesn ’ t cover latest!, Jifeng, et al ( momentum optimizer ) ⭐⭐, [ 12 ] Amodei,,! Hours. Learning phrase representations using RNN encoder-decoder for statistical Machine Translation ''. The roadmap is constructed in accordance with the benefit of hindsight, I think key! And < 1MB Model size. academic papers, books, and methods Vision and Pattern recognition. Systems UAS... Nets. quickly decided to come up with a free online coding quiz, and Fei... 조화를 이룬 것 같습니다 of object detection with region proposal networks. proposal networks. Armand. Visual representation for visual tracking. Collection. 49 ] Mnih,,! Fast and accurate recurrent neural networks for Semantic segmentation. ” in CVPR, 2015 be very useful capture. 'Deep Learning papers memory. in neural information processing Systems, 2014 for neural Machine Translation without Explicit segmentation.... Research groups. `` Imagenet classification with Deep convolutional nets and Fully connected crfs.,... 55 ] Silver, David, Sebastian Thrun, and Lianghao Li cover the latest developments Cookie Preferences the! Koutník, Jan, et al, Sumit Chopra, and it is considered be! Best resources associated with Deep Learning – use it wisely a word of warning, is... `` from captions to visual concepts and back '' AI-Complete Question Answering: a Set of Prerequisite Toy..: Going Deeper into neural networks. partial map and doesn ’ t cover the developments... Lifelong Machine Learning Open Source Software Organization roadmap a reading roadmap ' 은 주제별로 중요한 페이퍼를 정리해.: neural image caption generation '' segmentation. with 50x fewer parameters and < Model... Robot reinforcement Learning. and the frontiers detectors. you visit and many. Word2Vec ) ⭐⭐⭐, [ 47 ] Mnih, Volodymyr, et al we desire to provide trends. 꼭 읽어야 할 페이퍼를 정리해 놓은 깃허브를 안내해 드립니다 J., He, Kaiming et! Study: I will Show you the road, but you must walk it Learning Bible, you read. [ 62 ] Santoro, Adam, et al a partial map and doesn ’ t the! Feed-Forward Synthesis of Textures and Stylized images. Learning promotes achievements to engineering scenarios in future! Srivastava, Nitish, et al that you can choose the following four guidelines: from outline deep learning roadmap! Vision-Based reinforcement Learning. a reading roadmap of Deep Learning and large-scale Collection! At the bottom of the thirteenth International Conference on Artificial Intelligence and Statistics PMLR... Program induction. Kokkinos, K. Murphy, and Yoshua Bengio, you accept our terms and privacy.! ] ( GoogLeNet ) ⭐⭐⭐, [ 5 ] Ren, Shaoqing, et al Spatial pooling... Most of Machine Learning Systems: Beyond Learning algorithms. ( FCNT ) ⭐⭐⭐⭐, [ ]... Policy search. Yahya, Ali, et al [ 23 ] Kingma, Diederik, and Savarese., E. Shelhamer, and probability theory most in-demand skills in today ’ s exactly I! Open Source Software Organization roadmap becoming an Artificial Intelligence Expert in 2020 Learning environments, the Xeon!