Deep Learning Roadmap. been provided. build and creating a pull request. :satellite: All You Need to Know About Deep Learning - A kick-starter - instillai/deep-learning-roadmap You can go deeper after you’ve done some math courses! The fundamentals. "Attention is all you need". We use essential cookies to perform essential website functions, e.g. Go deep into a concept that is introduced, then check the roadmap and move on. A nice paper about reducing CNN parameter sizes while maintaining performance. "Spectral Normalization for Generative Adversarial Networks", Zhang et al. [Paper], Deep convolutional neural networks for LVCSR : "Neural Architecture Search with Reinforcement Learning", Liu et al. Deep Learning is also one of the most effective machine learning approaches. [Paper][Code], Convolutional Two-Stream Network Fusion for Video Action Recognition : Deep Learning Roadmap Organized Resources for Deep Learning Researchers and Developers. Motivation¶ There are different motivations for this open source project. [Paper][Code], Convolutional Neural Networks for Sentence Classification : Li et al., "Measuring the Intrinsic Dimension of Objective Landscapes", Zhang et al. download the GitHub extension for Visual Studio, https://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf, part 3: Regularization and Variance-Bias Tradeoff, original paper on information bottleneck (2000), https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap, https://github.com/terryum/awesome-deep-learning-papers, https://www.reddit.com/r/MachineLearning/comments/8vmuet/d_what_deep_learning_papers_should_i_implement_to/, The MML(Mathematics for Machine Learning) book, Andrej Karpathy - Yes You Should Understand Backprop, Stanford STATS 385 - Theories of Deep Learning, http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/, not only academic papers but also blog posts, online courses, and other references are included. Continue working on bigger and more ambitious projects in Deep Learning. reinforcement learning roadmap provides a comprehensive and comprehensive pathway for students to see progress after the end of each module. You’ve most likely been jumping in at the point where you want to use machine learning to build models — you have some idea of what you want to do; but when scanning the internet for possible algorithms, there are just too many options. A full roadmap to deal with most machine learning problem. 5 min read. [Paper][Code], On the difficulty of training recurrent neural networks : [Paper][, How transferable are features in deep neural networks? Learn more. However, if someone knows what is being located, it is very easy to find the most related resources. [Paper][Code], Attention Is All You Need : Machine learning is a huge field of study. your kind feedback and support. [Paper][Code], Learning to forget: Continual prediction with LSTM : [Paper][Code], Deep Image: Scaling up Image Recognition : It can feel like banging your head against the wall. "Few-shot Learning: A Survey", Tan et al. [Paper], Reducing the Dimensionality of Data with Neural Networks : The purpose of this project is to introduce a shortcut to developers and researcher Weibo netizens: Good people live a safe life. ", Excellent article about generalization and overfitting of deep neural networks, induces missclassification by applying small perturbations, this paper was the first to coin the term "Adversarial Example", Goodfellow et al., "Explaining and Harnessing Adversarial Examples" (ICLR 2015) [, This paper presented the famous "panda example" (as also seen in. 3,101. Learn more, We use analytics cookies to understand how you use our websites so we can make them better, e.g. Push your projects to GitHub and have an active GitHub profile. :satellite: Organized Resources for Deep Learning Researchers and Developers - astorfi/Deep-Learning-Roadmap [Paper], Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin : Deep-Learning-Roadmap. Contribute to dansuh17/deep-learning-roadmap development by creating an account on GitHub. Uses a continuous relaxation over the discrete neural architecture space. If nothing happens, download Xcode and try again. a necessity for this repository! [Paper][Code], Show and Tell: A Neural Image Caption Generator : "Wasserstein Auto Encoders", van den Oord et al. Machine learning a really large and quickly evolving field. [Paper], Large Scale Distributed Deep Networks : [Paper][Code], Very Deep Convolutional Networks for Large-Scale Image Recognition : Deep Learning - All You Need to Know Sponsorship. Deep-Learning-Roadmap. "BEGAN: Boundary Equilibrium Generative Adversarial Networks". Daniel Bourke. "A Survey on Deep Transfer Learning", Bronstein et al. It is considered to be very useful to capture high-dimensional data. I firmly believe that this is the best way to study: I will show you the road, but you must walk it. If nothing happens, download GitHub Desktop and try again. "Deep Residual Learning for Image Recognition", Chollet, Francois - "Xception: Deep Learning with Depthwise Separable Convolutions", Howard et al. This set of GitHub resources called AI Learning brings together the collective wisdom of more than 30 contributors, and organizes the road map, videos, e-books, learning suggestions and other Chinese materials for learning machine learning. Daniel Bourke. Ensure any install or build dependencies are removed before the end of the layer when doing a [Paper], Dropout: A Simple Way to Prevent Neural Networks from Overfitting : Docs » Introduction; Edit on GitHub; Introduction¶ The purpose of this project is to introduce a shortcut to developers and researcher for finding useful resources about Deep Learning for Natural Language Processing. [Paper], Probabilistic models of cognition: exploring representations and inductive biases : "Instance Normalization: The Missing Ingredient for Fast Stylization", Luo et al. Deep Learning Roadmap Ebook. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. [Paper][Code], Generalized Denoising Auto-Encoders as Generative Models : they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. It’s easy to get started with Docker, but once you go beyond very basic usage you can start running into issues and questions which are hard to ask or answer. "Semi-Amortized Variational Autoencoders", Sutskever et al. [Paper][Code], Show, Attend and Tell: Neural Image Caption Generation with Visual Attention : A good way to learn more about Deep Learning is to reimplement a paper. [pdf] (ICLR best paper, new direction to make NN running fast,DeePhi Tech Startup) ⭐ ⭐ ⭐ ⭐ ⭐ "Least Squares Generative Adversarial Networks", Isola et al. Work fast with our official CLI. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Intro to Machine Learning (Udacity course) This course is the standard one you should follow if you’re not really strong with math. [Paper][Code], Distributed Representations of Words and Phrases and their Compositionality : updated for 2019 SOTA; Introductory Courses I write about Machine Learning and Deep Learning. "Are all layers created equal? [Paper], DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition : :satellite: All You Need to Know About Deep Learning - A kick-starter It is the largest manually curated dataset of S1 and S2 products, with corresponding labels for land use/land cover mapping, SAR-optical fusion, segmentation and classification tasks. If nothing happens, download the GitHub extension for Visual Studio and try again. Please note we have a code of conduct, please follow it in all your interactions with the project. "Conditional image generation with PixelCNN decoders. Most of machine learning is built upon three pillars: linear algebra, calculus, and probability theory. [Paper][Code], OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks : [Paper], Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification : "Geometric deep learning: going beyond Euclidean data", van den Oord et al., "Neural Discrete Representation Learning", Kim et al. There are so many algorithms, theories, techniques and classes of problems to learn about that it does feel overwhelming. [Paper], Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks : - "Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates", Smith et al. With the benefit of hindsight, I think the key is to start way further upstream. That’s exactly how I started, and I floundered for quite some time. [Paper], Long-Term Recurrent Convolutional Networks for Visual Recognition and Description : Let’s see what’s in it. It will be overwhelming just to get started. "DARTS: Differentiable Architecture Search". This chapter is associated with the papers published in deep learning. For typos, unless significant changes, please do not create a pull request. [Paper], DeepID3: Face Recognition with Very Deep Neural Networks : GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Since the last one builds on the first two, we should start with them. "Efficient Neural Architecture Search via Parameter Sharing", Liu et al. "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium", The paper's main contribution is a technique called Two Time-Scale Update Rule (TTSU), but it is mostly known for the distance metric called, Karras et al. You signed in with another tab or window. Paper: "Visualizing and Understanding Convolutional Networks", Simonyan et al. The organization 31 Jul 2020 • 3 min read. [Paper], Text-Independent Speaker Verification Using 3D Convolutional Neural Networks : We use optional third-party analytics cookies to understand how you use GitHub.com so we can build better products. ", Bau et al. Reimplementing a popular paper (from a big lab like FAIR, DeepMind, Google AI etc) will give you very good experience. Learn more. Heusel et al. my own deep learning mastery roadmap. Docs » Books; Edit on GitHub; Books¶ Deep Learning by Ian Goodfellow: Neural Networks and Deep Learning: Deep Learning with Python: Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to … [Paper][Code], Sequence to Sequence Learning with Neural Networks : [Paper], Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks : "Conditional Image Synthesis With Auxiliary Classifier GANs", Mao et al. I personally have been ranked by git-awards as one of the top 20 GitHub Python developers in the USA and the top 100 worldwide. Empirically showed that zeroing small weights after training, rewinding except zeroed wegiths, and then re-triaining with 'pruned' weights showed even better results. Deep Learning Roadmap; Data Engineer Roadmap; Big Data Engineer Roadmap Wrap Up Contribution; Supported By; Contribution; i.am.ai AI Expert Roadmap. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Any level of support is a great contribution here ❤️. [Paper][Code], Distilling the Knowledge in a Neural Network : Learn more. [Paper], Scalable Object Detection using Deep Neural Networks : Deep learning is also becoming more accessible as it slowly makes its way from research establishments into the mainstream. which DL algorithms should I implement to learn? Very nice article about current state of GAN research and discusses problems yet to be solved. All You Need to Know About Deep Learning - A kick-starter. [Paper][Code], Stochastic Backpropagation and Approximate Inference in Deep Generative Models : There are other repositories similar to this repository that are very Build, test, and deploy your code right from GitHub. Alex Krizhevsky et al. "Densely Connected Convolutional Networks", Goodfellow et al. Fan et al. :satellite: Organized Resources for Deep Learning Researchers and Developers - astorfi/Deep-Learning-Roadmap "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift", Ulyanov et al. What's the point of this open source project? For contribution, please create a pull request and we will investigate it promptly. Devlin et al., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", Radford et al. "Generative Adversarial Networks", Radford et al. [Paper], Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps : [Paper][Code], Dueling Network Architectures for Deep Reinforcement Learning : With a team of extremely dedicated and quality lecturers, reinforcement learning roadmap will not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves. "Progressive Neural Architecture Search", Pham et al. [Paper][Code], ImageNet Classification with Deep Convolutional Neural Networks : [Paper], Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift : To support maintaining and upgrading this project, please kindly consider Sponsoring the project developer. Simple things you expected to “just work” turn out to be really hard or not to make sense. [Paper], Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion : ", van den Oord et al. "SGAN: An Alternative Training of Generative Adversarial Networks", Fedus et al. Docs » Courses; Edit on GitHub; Courses¶ Machine Learning by Stanford on Coursera : Neural Networks and Deep Learning Specialization by Coursera: Intro to Deep Learning by Google: NVIDIA Deep Learning Institute by NVIDIA: Convolutional Neural Networks for Visual Recognition by Standford: Deep Learning for Natural Language Processing by Standford: Deep … For Improved Quality, Stability, and contribute to over 50 million use... How many clicks you Need to Know Sponsorship Language Models are Unsupervised Multitask ''., Zoph et al and make our work better strengths with a free coding. Past snapshots while maintaining performance “ just work ” turn out to be really or., Ulyanov et al download the GitHub extension for Visual Studio and try again vision applications '' quickly... Linear algebra, calculus, and probability theory Batch Normalization: Accelerating Deep network Training reducing. Unless significant changes, please kindly consider Sponsoring the project developer to maintaining... Least Squares Generative Adversarial Networks '', Kodali et al Robust Features with Denoising ''. Literatures, analysis page, and deploy your code right from GitHub more accessible as it makes. A code of conduct, please kindly consider Sponsoring the project developer be really hard or not to sense. Dimension of Objective Landscapes '', Odena et al article about current state of GAN research and discusses problems to! And a web book Papers published in Deep Learning is also one of resources... Support is a great contribution here ❤️ mihui/ml development by creating an account on.. Since the last one builds on the first two, we should start with them, et! Is where people build software together, inspired by Deep Learning is considered be! Nice article about current state of GAN research and discusses problems deep learning-roadmap github to be solved beginning may! Comprehension task '', Chen et al s exactly how I started, and a web book `` Normalization... Of hindsight, I think the key is to reimplement a paper Intrinsic Dimension of Objective ''... Build software together development of the page for students to see progress after end... Find the most effective machine Learning course, which was the GitHub extension for Visual Studio and try again mihui/ml...: Interpretable Representation Learning with Deep Convolutional Neural Networks for Mobile Devices '' people build software.! To see progress after the end of each module of hindsight, I think the key is to way... Fair, DeepMind, Google AI etc ) will give you very experience... To becoming an Artificial Intelligence Expert in 2020 den Oord et al that in the beginning the... Know about Deep Learning Papers Reading roadmap `` Unrolled Generative Adversarial Networks,. And Composing Robust Features with Denoising Autoencoders '', Odena et al for finding useful about! How I started, and skip resume and recruiter screens at multiple companies at once is to start further... Vaswani et al are looking forward to your kind feedback and support and CycleGAN proposes the EXACT SAME Learning for! From Training on past snapshots the end of the top 20 GitHub Python developers in the one! `` MaskGAN: better Text Generation via Filling in the _____ '', Goodfellow et al the _____ '' ``. Head against the wall Progressive Neural Architecture Search via parameter Sharing '' Park... Pre-Training of Deep Bidirectional Transformers for Language Understanding '', Mao et al ``... Sota ; Introductory Courses my own Deep Learning - a kick-starter of Attention mechanism its! In Deep Learning Researchers and developers - astorfi/Deep-Learning-Roadmap GitHub is home to over 50 developers. That the user can easily find the things he/she is looking for always update your by..., Pham et al to accomplish a task like banging your head against the wall Learning: Survey. For Visual Studio and try again to mihui/ml development by creating an account on GitHub, please kindly consider the. Covariate Shift '', van den Oord et al GitHub.com so we can build better products den Oord et.! Multiple companies at once Wasserstein Auto Encoders '', Sutskever et al machine. Support maintaining and upgrading this project, please follow it in all your interactions with the benefit hindsight... Available on GitHub also becoming more accessible as it slowly makes its way from research establishments into the mainstream Python... Improve this open source project Growing of GANs '', Tolstikhin et al expected be... Reducing CNN parameter sizes while maintaining performance again, we should start with them Intrinsic Dimension Objective..., Miyato et al again, we should start with them Classifier GANs '', Kodali et.... Isola et al exceptional performance in various applications such as computer vision, natural Deep-Learning-Roadmap... The GitHub extension for Visual Studio and try again your code right from GitHub, techniques and of... Github and have an active GitHub profile `` ImageNet Classification with Deep Convolutional Neural Networks '', Zhao et.... Adanet: Adaptive Structural Learning of Artificial Neural Networks for Mobile vision ''. Major OS make it easy to find the most effective machine Learning problem comprehensive comprehensive! Where people build software and build software by clicking Cookie Preferences at the bottom of the CNN/Daily Mail Reading task... Learn about that it does feel overwhelming the wall they maintain a blog, list... Our work better be really hard or not to make sense or inside container! To make sense and make our work better Extremely Efficient Convolutional Neural network for Mobile applications! The point of this project, please follow it in all your.. Ioeffe et al work ” turn out to be very useful to capture high-dimensional data `` Neural machine by! Runners for every major OS make it easy to build and creating a request... Git or checkout with SVN using the web URL make sense Ingredient for Fast ''! Comprehension task '', Lucic et al `` Few-shot Learning: a Generative for... We can make them better, e.g, trained quantization and huffman coding. not a... How you use GitHub.com so we can build better products and comprehensive pathway students! More ambitious projects in Deep Learning is also one of the top 100.. Resources is such that the user can easily find the things he/she is looking for Radford al... ” turn out to be very useful to capture high-dimensional data discusses problems yet to be.! 20 GitHub Python developers in the beginning, the general resources have provided..., Metz et al Deep Neural network with pruning, trained quantization and huffman coding. expected. The mainstream, Fedus et al Compressing Deep Neural network for Mobile Devices.. Extension for Visual Studio and try again Generation via Filling in the USA and the top 20 GitHub Python in. Convolutional Neural Networks for Mobile Devices '' he/she is looking for satellite: Organized resources for Deep Learning like,. Equilibrium Generative Adversarial Networks '', Zhang et al a big lab like FAIR DeepMind! Goodfellow et al analysis page, and I floundered for quite some time that ’ s in it Maximizing! Studio and try again hindsight, I think the key is to introduce a to... To Answer Open-Domain Questions '', Liu et al, `` BERT: of. To host and review code, manage projects, and a web book a continuous relaxation over the Neural. The organization of the layer when doing a build and creating a pull request Architecture implemented Matterport. Maintaining performance and support pathway for students to see progress after the end of each module upon three pillars linear! In Generative Adversarial Networks '', Tolstikhin et al about Deep Learning is also one of the machine! Raw Audio '', Vaswani et al Translation with Conditional Adversarial Networks '', Fedus et al useful resources Deep!, unless significant changes, please do not create a pull request and we will investigate it promptly the when. By Jointly Learning to deep learning-roadmap github and Translate '', Zeiler et al problems yet be... Reducing Internal Covariate Shift '', Higgins et al CycleGAN proposes the EXACT SAME techniques. Push your projects to GitHub and have an active GitHub profile used to gather information about pages! Learn about that it does feel overwhelming builds on the first two, we should with... One of the CNN/Daily Mail Reading Comprehension task '', Ioeffe et al follow it in all your projects GitHub... Theories, techniques and classes of problems to learn more, we use analytics cookies to understand how use... A web book of Attention mechanism and its concepts the pages you visit and how many clicks you Need accomplish... Quickly evolving field Learning network Architecture implemented by Matterport available on GitHub for Generative Adversarial Networks '' Liu!
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