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[update 10/10/2017] Example of photo editing using inpainting at the project website. [update 9/30/2017] We shared the inpainting result of 200 ImageNet images and 100 Paris StreetView Images at the project website.

http://www.harryyang.org/inpaintinghttps://github.com/leehomyc/Faster-High-Res-Neural-Inpainting

Tags | deep-learning deep-learning-papers machine-learning |

Implementation | Lua |

License | MIT |

Platform |

We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep learning papers which are considered as must-reads in certain research domains. Before this list, there exist other awesome deep learning lists, for example, Deep Vision and Awesome Recurrent Neural Networks. Also, after this list comes out, another awesome list for deep learning beginners, called Deep Learning Papers Reading Roadmap, has been created and loved by many deep learning researchers.

deep-learning deep-neural-networks machine-learningawesome-very-deep-learning is a curated list for papers and code about implementing and training very deep neural networks. Value Iteration Networks are very deep networks that have tied weights and perform approximate value iteration. They are used as an internal (model-based) planning module.

highway-network deep-learning densenet resnet awesome-list machine-learning vinDeep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

neural-network machine-learning tensorflow keras deeplearning"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

machine-learning deep-learning text-analytics classification clustering natural-language-processing computer-vision data-science spacy nltk scikit-learn prophet time-series-analysis convolutional-neural-networks tensorflow keras statsmodels pandas deep-neural-networksRepository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks. Deep learning allows us to tackle complex problems, training artificial neural networks to recognize complex patterns for image and speech recognition. In this book, we'll continue where we left off in Python Machine Learning and implement deep learning algorithms in PyTorch.

deep-learning neural-network machine-learning tensorflow artificial-intelligence data-science pytorchA comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)

machine-learning deep-learning tensorflow pytorch keras matplotlib aws kaggle pandas scikit-learn torch artificial-intelligence neural-network convolutional-neural-networks tensorflow-tutorials python-data ipython-notebook capsule-networkArraymancer is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing and in particular a deep learning ecosystem. The library is inspired by Numpy and PyTorch. The library provides ergonomics very similar to Numpy, Julia and Matlab but is fully parallel and significantly faster than those libraries. It is also faster than C-based Torch.

tensor nim multidimensional-arrays cuda deep-learning machine-learning cudnn high-performance-computing gpu-computing matrix-library neural-networks parallel-computing openmp linear-algebra ndarray opencl gpgpu iot automatic-differentiation autogradSummaries of papers on deep learning.

deep-learning deep-neural-networks computer-vision machine-learning artificial-intelligenceThis repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources. Other awesome lists can be found in this list. If you want to contribute to this list, please read Contributing Guidelines.

deep-learning-tutorial machine-learning machinelearning deeplearning neural-network neural-networks deep-neural-networks awesome-list awesome list deep-learningChainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference. For more details of Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter. The stable version of current Chainer is separated in here: v3.

deep-learning neural-networks machine-learning gpu cuda cudnn numpy cupy chainer neural-networkKur is a system for quickly building and applying state-of-the-art deep learning models to new and exciting problems. Kur was designed to appeal to the entire machine learning community, from novices to veterans. It uses specification files that are simple to read and author, meaning that you can get started building sophisticated models without ever needing to code. Even so, Kur exposes a friendly and extensible API to support advanced deep learning architectures or workflows.

deep-learning deep-neural-networks speech-recognition deep-learning-tutorial machine-learning neural-networks neural-network image-recognition speech-to-textA generic image detection program that uses Google's Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception. This model has been pre-trained for the ImageNet Large Visual Recognition Challenge using the data from 2012, and it can differentiate between 1,000 different classes, like Dalmatian, dishwasher etc. The program applies Transfer Learning to this existing model and re-trains it to classify a new set of images.

image-detection machine-learning deep-learning deep-neural-networks convolutional-neural-networks tensorflowSome examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

recurrent-neural-networks convolutional-neural-networks deep-learning-tutorial tensorflow tensorlayer keras deep-reinforcement-learning tensorflow-tutorials deep-learning machine-learning notebook autoencoder multi-layer-perceptron reinforcement-learning tflearn neural-networks neural-network neural-machine-translation nlp cnnTensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides a large collection of customizable neural layers / functions that are key to build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. Simplicity : TensorLayer lifts the low-level dataflow interface of TensorFlow to high-level layers / models. It is very easy to learn through the rich example codes contributed by a wide community.

tensorlayer deep-learning tensorflow machine-learning data-science neural-network reinforcement-learning artificial-intelligence gan a3c tensorflow-tutorials dqn object-detection chatbot tensorflow-tutorial imagenet googlePapers about deep learning ordered by task, date. Current state-of-the-art papers and papers useful for getting started are labelled. For each paper there is a permanent link, which is either to Arxiv.org or to a copy of the original paper in this repository.

deep-learning neural-networksTrending deep learning Github repositories can be found here. Hint: This will be updated regularly.

deep-learning deep-neural-networks deep-reinforcement-learning convolutional-neural-networks recurrent-neural-networks stargazers-count artificial-neural-networks artificial-intelligence machine-learning top-repositoriesTFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. The high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks... In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques.

tflearn tensorflow neural-network deep-learning machine-learning data-scienceThis repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. These topics are organised into three high level themes forming a progression from understanding the use of neural networks for sequential language modelling, to understanding their use as conditional language models for transduction tasks, and finally to approaches employing these techniques in combination with other mechanisms for advanced applications. Throughout the course the practical implementation of such models on CPU and GPU hardware is also discussed.

deep-learning machine-learning natural-language-processing nlp oxfordBy Yann Bayle (Website, GitHub) from LaBRI (Website, Twitter), Univ. Bordeaux (Website, Twitter), CNRS (Website, Twitter) and SCRIME (Website). The role of this curated list is to gather scientific articles, thesis and reports that use deep learning approaches applied to music. The list is currently under construction but feel free to contribute to the missing fields and to add other resources! To do so, please refer to the How To Contribute section. The resources provided here come from my review of the state-of-the-art for my PhD Thesis for which an article is being written. There are already surveys on deep learning for music generation, speech separation and speaker identification. However, these surveys do not cover music information retrieval tasks that are included in this repository.

awesome awesome-list unicorns list lists resources deeplearning deep-learning deep-neural-networks neural-network neural-networks music music-information-retrieval audio audio-processing article music-genre-classification bib machine-learning researchTo the best of our knowledge, this is the first list of deep learning papers on medical applications. There are couple of lists for deep learning papers in general, or computer vision, for example Awesome Deep Learning Papers. In this list, I try to classify the papers based on their deep learning techniques and learning methodology. I believe this list could be a good starting point for DL researchers on Medical Applications.

deep-learning medical-imaging medical-informatics awesome-list
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