Displaying 1 to 19 from 19 results

emoj - Find relevant emoji from text on the command-line :open_mouth: :sparkles: :raised_hands: :horse: :boom: :see_no_evil:

  •    Javascript

Uses the API from this great article on Emoji & Deep Learning. Check out the Dango app if you want something like this on your phone.Works best on macOS. Terminals on Linux render emojis in monochrome as they don't support color emojis. On Linux, I would recommend installing Emoji One for full emoji coverage. Doesn't really work on Windows.

keras-js - Run Keras models in the browser, with GPU support using WebGL

  •    Javascript

**This project is no longer active. Please check out TensorFlow.js.** The Keras.js demos still work but is no longer updated. Run Keras models in the browser, with GPU support provided by WebGL 2. Models can be run in Node.js as well, but only in CPU mode. Because Keras abstracts away a number of frameworks as backends, the models can be trained in any backend, including TensorFlow, CNTK, etc.

networkD3 - D3 JavaScript Network Graphs from R

  •    R

This README includes information on set up and a number of basic examples. For more information see the package's main page. networkD3 began as a port of d3Network package to the htmlwidgets framework. d3Network is no longer supported.




regl-cnn - Digit recognition with Convolutional Neural Networks in WebGL

  •    Javascript

GPU accelerated handwritten digit recognition with regl. Note that this network will probably be slower than the corresponding network implemented on the CPU. This is because of the overhead associated with transferring data to and from the GPU. But in the future we will attempt implementing more complex networks in the browser, such as Neural Style, and then we think that we will see a significant speedup compared to the CPU.

nodewatcher - A modular open networks growing platform.

  •    Python

nodewatcher is one of the projects of wlan slovenija open wireless network. Its main goal is the development of an open source network planning, deployment, monitoring and maintanance platform with emphasis on community. This is the development branch with future (3.0) version of nodewatcher which is still being developed and does not have all the functionality found in the 2.0 version. For stable 2.0 version, switch to the master branch.

pathpy - pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models

  •    Python

pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models. The package is specifically tailored to analyze sequential data which capture multiple observations of short, independent paths observed in an underlying graph topology. Examples for such data include user click streams in information networks, biological pathways, or traces of information propagating in social media. Unifying the analysis of pathways and temporal networks, pathpy also supports the extraction of time-respecting paths from time-stamped network data. It extends (and will eventually supersede) the package pyTempnets.

Penetration-Testing-Tools - Great collection of my Penetration Testing scripts, tools, cheatsheets collected over years, used during real-world assignments or collected from various good quality sources

  •    Python

This is a collection of many tools, scripts, cheatsheets and other loots that I've been developing over years for penetration testing and IT Security audits purposes. Many of them actually had been used during real-world assignments, some of them are a collection gathered from various sources (waiting to be used someday). This repository does not contain actual exploits. These I will release under separate repository in some point in future.


machine_learning_and_neuroscience - This is the code for "Machine Learning & Neuroscience" By Siraj Raval on Youtube

  •    Javascript

This is the code from this video on Youtube by Siraj Raval. An interactive network visualization tool for exploring functional brain connectivity using d3.js. See this for an example of SpectraVis in action.

gephi-tutorials - Open and collaborative tutorials for Gephi

  •    Java

written in asciidoc to ease the conversion to pdf, html, slides, etc. tutorials use a lot of charts. I use Google Drawings for that. Very practical for online editing, and live updates: charts made on Google Drawing can be embedded in web documents, so that any correction brought to a chart is instantaneously reflected in the web versions of the tutorials. Great stuff.

hetmech - Matrix-based storage & computation on hetnets to enable a search engine for hetnets

  •    Jupyter

Hetmech aims to identify the relevant network connections between a set of query nodes. The method is designed to operate on hetnets (networks with multiple node or relationship types). This project is still under development. Use with caution.

EcologicalNetworks.jl - Everything you've never dreamed about measuring on ecological networks.

  •    Julia

This julia package provides a common interface to analyze all types of data on ecological networks. It is designed to be general, easy to expand, and work on bipartite/unipartite as well as deterministic/quantitative/probabilistic networks. The current version is compatible with julia v0.7 -- the latest version compatible with julia v0.6 is 1.2.0. That's it. Now head over to the documentation.

geomnet - Examples and data for geom_net

  •    R

geomnet is a package built on top of the most recent major ggplot2 release. It provides a ggplot2 geom called geom_net to visualize graphs and networks. It also include the function stat_net to calculate network layouts with the sna package. Finally, the function geom_circle is included to draw circles using ggplot2. This example shows the theme inheritance properties of the theme elements of ggplot2. Note: this example has not been updated since the release of ggplot2 2.2.0 and as such the content may have changed.

SCope - Fast visualization tool for large-scale and high dimensional single-cell data

  •    Javascript

SCope is a fast visualization tool for large-scale and high dimensional scRNA-seq datasets. Currently the data format supported by SCope is .loom. This file format for very large omics datasets is maintained by the Linnarsson Lab through the loompy Python package (https://github.com/linnarsson-lab/loompy). Visit http://scope.aertslab.org to test out SCope on several published datasets! Personal loom file files can be uploaded but will only be kept for 5 days.

feedforward-neural-networks - A implementation of feedforward neural networks based on wildml implementation

  •    Javascript

A implementation of feedforward neural networks in javascript based on wildml implementation.

mlxsh - mlxsh is the missing, fast power command-line and shell that enables you to enter configuration changes or run commands simultaneously to groups of Brocade or Extreme Networks Netiron devices (MLX, CER, MLXE, XMR, Ironware), SLX-Devices or Juniper switches via Secure Shell (ssh)

  •    Go

mlxsh is the missing power command-line that enables you to enter configuration changes or operating commands to groups of Brocade / Extreme Networks Netiron devices (MLX, MLXE, CER, XMR), other Ironware style devices like Turboiron, ICX and also SLX/VDX switches and new (since 0.3) also for Juniper switches. SLX support.