Displaying 1 to 20 from 77 results

ipfs - Peer-to-peer hypermedia protocol

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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.

oriDomi - Fold up DOM elements like paper

  •    CoffeeScript

Visit oridomi.com for examples, documentation and notes. Read the annotated source for a detailed look.

ember-paper - The Ember approach to Material Design.

  •    Javascript

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.




awesome-distributed-systems - A curated list to learn about distributed systems

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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.

Paper - Paper is a fast NoSQL-like storage for Java/Kotlin objects on Android with automatic schema migration support

  •    Java

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.

PyCNN - Image Processing with Cellular Neural Networks in Python

  •    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.


Personae - 📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading

  •    Python

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.

DenseNet - DenseNet implementation in Keras

  •    Python

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.

Quttons - Buttons made of Quantum Paper

  •    Javascript

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.

Conditional-PixelCNN-decoder - Tensorflow implementation of Gated Conditional Pixel Convolutional Neural Network

  •    Python

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.

All-About-the-GAN - All About the GANs(Generative Adversarial Networks) - Summarized lists for GAN

  •    Python

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.

LanguageDetector - PHP Class to detect languages from any free text

  •    PHP

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.

awesome-transfer-learning - Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc

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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.

Inception-v4 - Inception-v4, Inception - Resnet-v1 and v2 Architectures in Keras

  •    Python

Implementations of the Inception-v4, Inception - Resnet-v1 and v2 Architectures in Keras using the Functional API. The paper on these architectures is available at "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning". The models are plotted and shown in the architecture sub folder. Due to lack of suitable training data (ILSVR 2015 dataset) and limited GPU processing power, the weights are not provided.