PyTorchis an open source machine learning library for Python, based on Torch, used for applications such as natural language processing. It is primarily developed by Facebook's artificial-intelligence research group, and Uber's "Pyro" software for probabilistic programming is built on it.
PyTorch is a python package that provides two high-level features:
Tensor computation (like numpy) with strong GPU acceleration
Deep Neural Networks built on a tape-based autodiff system
PyTorch has a unique way of building neural networks: using and replaying a tape recorder.
Most frameworks such as TensorFlow, Theano, Caffe and CNTK have a static view of the world. One has to build a neural network, and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch.
With PyTorch, we use a technique called Reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead.
Our inspiration comes from several research papers on this topic, as well as current and past work such as autograd, autograd, Chainer, etc.
Plone CMS is an open source Content Management System for managing information and administering content. Plone is backed by Plone Foundation - international non-profit organization. The organization holds the copyright, and Plone Content Management System is available under a dual licensing scheme, GPL and a commercial license.
Plone Content Management System was founded in 1999 by Alan Runyan (USA), Alexander Limi (Norway) and Vidar Andersen (Norway). Plone has 200 core developers and more than 300 solution providers in 57 different countries.
Plone CMS is built on top of the Zope web application server and Zope's Content Management Framework, written in Python. Plone Content Management System is ideal as an intranet server, as a document publishing system and as a groupware tool for collaboration between separately located entities. A versatile software product like Plone Content Management System can be used in a myriad of ways. Plone works on top of Linux, Windows, Mac OSX, and other Unix variants.
INDUSTRIAL STRENGTH SECURITY
Object-oriented navigation – Plone is an object-oriented system that uses folder-based navigation with human-readable URLs. Customizable navigation portlets offer flexible user guidance.
Search engine optimization – The compliance to web standards, as well as the automatic production of machine-readable sitemaps make Plone a search engine-optimized system.
Multilingual – Plone is designed for international use, featuring over 50 different languages, including Arabic, Hebrew and Chinese.
Internal search engine – An internal search engine, featuring advanced options facilitates finding specific information instantaneously. Various search engines (e. g. Solr GSA) can be plugged in via add-ons.
Social networking – Plone supports social networking by automatically generating feeds out of search results and folder contents. A wide range of extensions and add-on products integrate Plone into other social networks.
Accessibility – Plone is accessible and complies to WAI-AA standard and the U.S. Government Section 508. Since public institutions are legally obliged to offering barrier-free websites, Plone can perfectly assist on these efforts – including a barrier-free UI for editors as well.
Nagare is a free and open-source web framework for developing web applications in Stackless Python. This allows web applications to be developed in much the same way as desktop applications, for rapid application development.
Nagare is a components based framework: a Nagare application is a composition of interacting components each one with its own state and workflow kept on the server.
Each component can have one or several views that are composed to generate the final web page. This enables the developers to reuse or write highly reusable components easily and quickly.
Nagare is also a continuation-based web framework which enables to code a web application like a desktop application, with no need to split its control flow in a multitude of controllers and with the automatic handling of the back, fork and refresh actions from the browser.
Its component model and use of the continuation come from the famous Seaside Smalltalk framework.
Furthermore, Nagare integrates the best tools and standard from the Python world. For example:
WSGI: binds the application to several possible publishers,
lxml: generates the DOM trees and brings to Nagare the full set of XML features (XSL, XPath, Schemas …),
setuptools: installs, deploys and extends the Nagare framework and the Nagare applications too,
Pyramid is a Python web application framework. It is designed to make creating web applications easier. It is open source. A pyramid is fully compatible with Python 3.
With Pyramid, we can write very small applications without needing to know a lot. And by learning a bit more, you can write very large applications too.
Pyramid will allow you to become productive quickly and will grow with you. It won't hold you back when your application is small, and it won't get in your way when your application becomes large. Other application frameworks seem to fall into two non-overlapping categories: those that support "small apps" and those designed for "big apps".
Pyramid can automatically detect changes you make to template files and code, so your changes are immediately available in your browser. You can debug using plain old print() calls, which will display to your console.
Pyramid has a debug toolbar that allows you to see information about how your application is working right in your browser. See configuration, installed packages, SQL queries, logging statements and more.
Anaconda is a freemium open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment
The condacommand is the primary interface for managing installations of various packages. It can:
Query and search the Anaconda package index and current Anaconda installation.
Create new conda environments.
Install and update packages into existing conda environments.
Anaconda Cloud is where data scientists share their work. You can search and download popular Python and R packages and notebooks to jumpstart your data science work.
Anaconda is the world’s most popular Python data science platform. Anaconda, Inc. continues to lead open source projects like Anaconda, NumPy and SciPy that form the foundation of modern data science. Anaconda’s flagship product, Anaconda Enterprise, allows organizations to secure, govern, scale and extend Anaconda to deliver actionable insights that drive businesses and industries forward.
Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions.
Flask supports extensions that can add application features as if they were implemented in Flask itself. Extensions exist for object-relational mappers, form validation, upload handling, various open authentication technologies and several common framework related tools. Extensions are updated far more regularly than the core Flask program.
Flask is a web framework. This means flask provides you with tools, libraries, and technologies that allow you to build a web application. This web application can be some web pages, a blog, a wiki or go as big as a web-based calendar application or a commercial website.
Flask is part of the categories of the micro-framework. Micro-framework is normally framework with little to no dependencies to external libraries. This has pros and cons. Pros would be that the framework is light, there is little dependency to update and watch for security bugs, cons is that sometime you will have to do more work by yourself or increase yourself the list of dependencies by adding plugins. In the case of Flask, its dependencies are:
FastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. FastText builds on modern Mac OS and Linux distributions. Since it uses C++11 features, it requires a compiler with good C++11 support.
Steps for Installing
- git clone https://github.com/facebookresearch/fastText.git - cd fastText - make
Text classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool.
What is text classification? The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc...) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed.
Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels).