Displaying 1 to 20 from 112 results

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.

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


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.

paperless - Scan, index, and archive all of your paper documents

  •    Python

It's been more than 5 years since I started this project on a whim as an effort to try to get a handle on the massive amount of paper I was dealing with in relation to various visa applications (expat life is complicated!) Since then, the project has exploded in popularity, so much so that it overwhelmed me and working on it stopped being "fun" and started becoming a serious source of stress. In an effort to fix this, I created the Paperless GitHub organisation, and brought on a few people to manage the issue and pull request load. Unfortunately, that model has proven to be unworkable too. With 23 pull requests waiting and 157 issues slowly filling up with confused/annoyed people wanting to get their contributions in, my whole "appoint a few strangers and hope they've got time" idea is showing my lack of foresight and organisational skill.

jukebox - Code for the paper "Jukebox: A Generative Model for Music"

  •    Python

The samples decoded from each level are stored in {name}/level_{level}. You can also view the samples as an html with the aligned lyrics under {name}/level_{level}/index.html. Run python -m http.server and open the html through the server to see the lyrics animate as the song plays. A summary of all sampling data including zs, x, labels and sampling_kwargs is stored in {name}/level_{level}/data.pth.tar. The hps are for a V100 GPU with 16 GB GPU memory. The 1b_lyrics, 5b, and 5b_lyrics top-level priors take up 3.8 GB, 10.3 GB, and 11.5 GB, respectively. The peak memory usage to store transformer key, value cache is about 400 MB for 1b_lyrics and 1 GB for 5b_lyrics per sample. If you are having trouble with CUDA OOM issues, try 1b_lyrics or decrease max_batch_size in sample.py, and --n_samples in the script call.

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.

random-network-distillation - Code for the paper "Exploration by Random Network Distillation"

  •    Python

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).

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.

Surface-Defect-Detection - 📈 Constantly summarizing open source dataset and critical papers in the field of surface defect research which are of great importance

  •    Python

At present, surface defect equipment based on machine vision has widely replaced artificial visual inspection in various industrial fields, including 3C, automobiles, home appliances, machinery manufacturing, semiconductors and electronics, chemical, pharmaceutical, aerospace, light industry and other industries. Traditional surface defect detection methods based on machine vision often use conventional image processing algorithms or artificially designed features plus classifiers. Generally speaking, imaging schemes are usually designed by using the different properties of the inspected surface or defects. A reasonable imaging scheme helps to obtain images with uniform illumination and clearly reflect the surface defects of the object. In recent years, many defect detection methods based on deep learning have also been widely used in various industrial scenarios. Compared with the clear classification, detection and segmentation tasks in computer vision, the requirements for defect detection are very general. In fact, its requirements can be divided into three different levels: "what is the defect" (classification), "where is the defect" (positioning) And "How many defects are" (split).

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.

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