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ipfs - Peer-to-peer hypermedia protocol
A peer-to-peer hypermedia protocol to make the web faster, safer, and more open.IPFS (the InterPlanetary File System) is a new hypermedia distribution protocol, addressed by content and identities. IPFS enables the creation of completely distributed applications. It aims to make the web faster, safer, and more open.
ipfs p2p ipld multiformats js-ipfs protocol paper ipfs-protocol ipfs-webember-paper - The Ember approach to Material Design.
This project aims to bring Google's new Material Design to Ember. The goal is to encapsulate everything possible in Ember components. This project is packaged as an Ember-cli addon. This should also automatically create an scss file under app/styles/app.scss with @import 'ember-paper'; and install ember-cli-sass.
ember-paper ember ember-addon material-design material design paperawesome-distributed-systems - A curated list to learn about distributed systems
A (hopefully) curated list on awesome material on distributed systems, inspired by other awesome frameworks like awesome-python. Most links will tend to be readings on architecture itself rather than code itself. Read things here before you start.
distributed-systems paper architecture paxos lamport consensusPaper - Paper is a fast NoSQL-like storage for Java/Kotlin objects on Android with automatic schema migration support
Paper's aim is to provide a simple yet fast object storage option for Android. It allows to use Java/Kotlin classes as is: without annotations, factory methods, mandatory class extensions etc. Moreover adding or removing fields to data classes is no longer a pain – all data structure changes are handled automatically. Save any object, Map, List, HashMap etc. including all internal objects. Use your existing data classes as is.
mobile-database nosql android database performace paperdaily-paper-computer-vision - 记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文
记录每天整理的计算机视觉/深度学习/机器学习相关方向的论文
paper deep-learning computer-vision machine-learning face-detection object-detectionPyCNN - Image Processing with Cellular Neural Networks in Python
Cellular Neural Networks (CNN) [wikipedia] [paper] are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors. This python library is the implementation of CNN for the application of Image Processing.
cellular neural-network cnn image-processing cnn-processors paper edge-detection corner-detection library cross-platform feedback computer-vision computer-science controlPersonae - 📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading
Personae is a repo that implements papers proposed methods in Deep Reinforcement Learning & Supervised Learning and applies them to Financial Market. It will start from 2018-08-24 to 2018-09-01 a timestamp that I successfully found a job.
reinforcement-learning supervised-learning stock-data trading paper stock time-series-prediction stock-price-predictionrandom-network-distillation - Code for the paper "Exploration by Random Network Distillation"
To use more than one gpu/machine, use MPI (e.g. mpiexec -n 8 python run_atari.py --num_env 128 --gamma_ext 0.999 should use 1024 parallel environments to collect experience on an 8 gpu machine).
paperDenseNet - DenseNet implementation in Keras
The Bottleneck - Compressed DenseNets offer further performance benefits, such as reduced number of parameters, with similar or better performance. The best original model, DenseNet-100-24 (27.2 million parameters) achieves 3.74 % error, whereas the DenseNet-BC-190-40 (25.6 million parameters) achieves 3.46 % error which is a new state of the art performance on CIFAR-10.
densenet densenet-model paper bottleneck deep-learning kerasQuttons - Buttons made of Quantum Paper
Qunatum Paper is a digital paper that can change its size, shape and color to accommodate new content. Quantum paper is part of Google's new Material Design language. Switch to gh-pages branch to look at code used in demo site.
quantum paper buttonsConditional-PixelCNN-decoder - Tensorflow implementation of Gated Conditional Pixel Convolutional Neural Network
This is a Tensorflow implementation of Conditional Image Generation with PixelCNN Decoders which introduces the Gated PixelCNN model based on PixelCNN architecture originally mentioned in Pixel Recurrent Neural Networks. The model can be conditioned on latent representation of labels or images to generate images accordingly. Images can also be modelled unconditionally. It can also act as a powerful decoder and can replace deconvolution (transposed convolution) in Autoencoders and GANs. A detailed summary of the paper can be found here. The gating accounts for remembering the context and model more complex interactions, like in LSTM. The network stack on the left is the Vertical stack that takes care of blind spots that occure while convolution due to the masking layer (Refer the Pixel RNN paper to know more about masking). Use of residual connection significantly improves the model performance.
deep-learning generative-algorithm paper convolution deepmind tensorflowng-polymer-elements - Extendible AngularJS two-way binding support for Polymer elements and web components
Looking for maintainers, please contact me if you are interested.
angular angularjs polymer paper web-componentspapers-notebook - :page_facing_up: :cn: :page_with_curl: 论文阅读笔记（分布式，虚拟化，容器，自动机器学习）
:page_facing_up: :cn: :page_with_curl: 论文阅读笔记（分布式，虚拟化，容器，自动机器学习）
paper notes class computer-system operating-system chinese gaocegegeAll-About-the-GAN - All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN
The purpose of this repository is providing the curated list of the state-of-the-art works on the field of Generative Adversarial Networks since their introduction in 2014. You can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title here.
gan adversarial-networks arxiv neural-network unsupervised-learning adversarial-nets image-synthesis deep-learning generative-adversarial-network medical-imaging tensorflow pytorch paper cgan ct-denoising segmentation medical-image-synthesis reconstruction detection classificationLanguageDetector - PHP Class to detect languages from any free text
PHP Class to detect languages from any free text. It follows the approach described in the paper, a given text is tokenized into N-Grams (we cleanup whitespaces before doing this step). Then we sort the tokens and we compare against a language model.
languagedetector detect-languages paper textrankawesome-transfer-learning - Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc
A list of awesome papers and cool resources on transfer learning, domain adaptation and domain-to-domain translation in general! As you will notice, this list is currently mostly focused on domain adaptation (DA), but don't hesitate to suggest resources in other subfields of transfer learning. I accept pull requests. Papers are ordered by theme and inside each theme by publication date (submission date for arXiv papers). If the network or algorithm is given a name in a paper, this one is written in bold before the paper's name.
transfer-learning domain-adaptation unsupervised-learning paper awesome-listMinecraftDev - Plugin for IntelliJ IDEA that gives special support for Minecraft modding projects.
Visit https://minecraftdev.org for a little information about the project. This plugin is available on the JetBrains IntelliJ plugin repository.
intellij jetbrains minecraft bukkit spigot paper sponge forge liteloader bungeecord mcp mixins canary neptune kotlin gradle gradle-kotlin-dslAmber - Reflective PE packer.
amber is a reflective PE packer for bypassing security products and mitigations. It can pack regularly compiled PE files into reflective payloads that can load and execute itself like a shellcode. It enables stealthy in-memory payload deployment that can be used to bypass anti-virus, firewall, IDS, IPS products and application white-listing mitigations. If you want to learn more about the packing methodology used inside amber check out below. For more detail about usage, installation and how to decrease detection rate check out WIKI. Developed By Ege Balcı from INVICTUS/PRODAFT.
packer pe crypter stub shellcode shellcode-loader payload malware-research paper
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