SINGA is an Apache Incubating project for developing an open source machine learning library. It provides a flexible architecture for scalable distributed training, is extensible to run over a wide range of hardware, and has a focus on health-care applications.
SINGA was initiated by the DB System Group at National University of Singapore in 2014, in collaboration with the database group of Zhejiang University.
SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning.
The second goal is to make SINGA easy to use. It is non-trivial for programmers to develop and train models with deep and complex model structures. Distributed training further increases the burden of programmers, e.g., data and model partitioning, and network communication. Hence it is essential to provide an easy to use programming model so that users can implement their deep learning models/algorithms without much awareness of the underlying distributed platform.
What is Seaborn? Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
A dataset-oriented API for examining relationships between multiple variables
Specialized support for using categorical variables to show observations or aggregate statistics
Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data
Automatic estimation and plotting of linear regression models for different kinds dependent variables
Convenient views onto the overall structure of complex datasets
High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations
Concise control over matplotlib figure styling with several built-in themes
Tools for choosing color palettes that faithfully reveal patterns in your data
Seaborn aims to make visualization a central part of exploring and understanding data. Its dataset-oriented plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.
mlpack is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users.
This is done by providing a set of command-line executables which can be used as black boxes, and a modular C++ API for expert users and researchers to easily make changes to the internals of the algorithms.
As a result of this approach, mlpack outperforms competing machine learning libraries by large margins; see the BigLearning workshop paper and the benchmarks for details.
mlpack is developed by contributors from around the world. It is released free of charge, under the 3-clause BSD License (more information). (Versions older than 1.0.12 were released under the GNU Lesser General Public License: LGPL, version 3.)
mlpack was originally presented at the BigLearning workshop of NIPS 2011 [pdf] and later published in the Journal of Machine Learning Research [pdf], with version 3 being published in the Journal of Open Source Software [pdf]. Please cite mlpack in your work using this citation.
mlpack bindings for R are provided by the RcppMLPACK project.
Currently mlpack supports the following algorithms:
Decision stumps (one-level decision trees)
Density Estimation Trees
Euclidean Minimum Spanning Trees
Gaussian Mixture Models (GMMs)
Hidden Markov Models (HMMs)
Kernel Principal Component Analysis (KPCA)
Least-Angle Regression (LARS/LASSO)
Local Coordinate Coding
Locality-Sensitive Hashing (LSH)
Naive Bayes Classifier
Nearest neighbor search with dual-tree algorithms
Neighbourhood Components Analysis (NCA)
Non-negative Matrix Factorization (NMF)
Principal Components Analysis (PCA)
Independent component analysis (ICA)
Rank-Approximate Nearest Neighbor (RANN)
Simple Least-Squares Linear Regression (and Ridge Regression)
Sparse Coding, Sparse dictionary learning
For more detail visit here - http://mlpack.org/docs.html
PyShark is a wrapper for the Wireshark CLI interface, tshark, so all of the Wireshark decoders are available to PyShark!
Python wrapper for tshark, allowing python packet parsing using wireshark dissectors.
There are quite a few python packet parsing modules, this one is different because it doesn't actually parse any packets, it simply uses tshark's (wireshark command-line utility) ability to export XMLs to use its parsing.
This package allows parsing from a capture file or a live capture, using all wireshark dissectors you have installed. Tested on windows/linux.
Example Code for Reading a File
import pyshark cap = pyshark.FileCapture('/tmp/mycapture.cap') cap >>> <FileCapture /tmp/mycapture.cap> print cap Packet (Length: 698) Layer ETH: Destination: aa:bb:cc:dd:ee:ff Source: 00:de:ad:be:ef:00 Type: IP (0x0800) Layer IP: Version: 4 Header Length: 20 bytes Differentiated Services Field: 0x00 (DSCP 0x00: Default; ECN: 0x00: Not-ECT (Not ECN-Capable Transport)) Total Length: 684 Identification: 0x254f (9551) Flags: 0x00 Fragment offset: 0 Time to live: 1 Protocol: UDP (17) Header checksum: 0xe148 [correct] Source: 192.168.0.1 Destination: 192.168.0.2