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.
Statsmodels is a Python package that allows users to explore data, estimate statistical models, and perform statistical tests. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy for an R-like formula interface.
Statsmodels is part of the scientific Python stack that is oriented towards data analysis, data science and statistics. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy for an R-like formula interface. Graphical functions are based on the Matplotlib library. Statsmodels provides the statistical backend for other Python libraries. Statmodels in free software released under the Modified BSD (3-clause) license.
Linear regression models:
Mixed Linear Model with mixed effects and variance components
GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
Bayesian Mixed GLM for Binomial and Poisson
GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
Nonparametric statistics: Univariate and multivariate kernel density estimators
Datasets: Datasets used for examples and in testing
Statistics: a wide range of statistical tests
Imputation with MICE, regression on order statistic and Gaussian imputation
Tools for reading Stata .dta files, but pandas has a more recent version
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
Supports both convolutional networks and recurrent networks, as well as combinations of the two.
Runs seamlessly on CPU and GPU.
User friendliness. Keras is an API designed for human beings, not machines. It puts user experience front and center. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.
Modularity. A model is understood as a sequence or a graph of standalone, fully-configurable modules that can be plugged together with as few restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models.
Easy extensibility. New modules are simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
Work with Python. No separate models configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility
webOS, also known as LG webOS and previously known as Open webOS, HP webOS and Palm webOS, is a Linux kernel-based multitasking operating system for smart devices such as smart TVs and it has been used as a mobile operating system. Initially developed by Palm, Inc. (which was acquired by Hewlett-Packard), HP made the platform open source, at which point it became Open webOS.
The operating system was later sold to LG Electronics. In January 2014, Qualcomm announced that it had acquired technology patents from HP, which included all the webOS and Palm patents.
Web Operating System is an internet service through which a user can access his computer data remotely anywhere on any computer and in any part of earth were internet is available.
WebOS is an LG-owned, Linux-based, smart TV operating system that is set up to allow control and access of LG Smart TV’s more advanced features and connected devices through a graphical user interface (GUI).
WebOS was developed by Palm as a mobile OS. The company released it in 2009 as Palm webOS. Hewlett Packard acquired palm in April 2010.
The operating system was used in a number of Palm and HP smartphones before being modified for use in HP tablet PCs, such as the TouchPad. After the TouchPad failed to gain market share, HP made webOS open source. LG purchased webOS in February 2013 and modified it as a smart TV operating system.
Of particular interest to LG were webOS’s cloud integration and its easy adaptation to LG’s Magic Motion remote. John I. Taylor, Vice President of Public Affairs and Communications at LG Electronics, said LG intends to use webOS in other smart appliances. LG has not ruled out future use in smartphones.
It has been termed as “Web Operating System” because they are present on the web and not on the computer of the user, all the data is being stored on the servers of the Web OS provider.
WebOS are the dynamic computers. The applications, hard disk, operating systems are all present at the servers from where they are operated. The web OS service provider has different spaces for application access and database. The user is provided with a graphical user interface which feels like the one at your PC. This operating system consists of application section like calendar, clock, calculator, document editors etc then there is a section for data storage where user can store data, and there are many other sections depending upon the web OS. Whatever content user wants to store is stored at the hard disk at servers. As the terminology itself says, the web OS make use of the web to connect and upload files to the client server.
Webware for Python. Webware for Python is an object-oriented, Python web application framework. The suite uses well known design patterns and includes a fast application server, servlets, Python Server Pages (PSP), object-relational mapping, Task Scheduling, Session Management, and many other features.
Webware for Python is a suite of programming tools for constructing web-based applications in Python. It features:
Traditional web development tools:
Python-based Server Pages
Object-relational mapping (ORM)
Webware for Python is well proven and platform-independent. It is compatible with multiple web servers, database servers, and operating systems.
Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
Simple and efficient tools for data mining and data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable - BSD license
scikit-learn comes with a few standard datasets, for instance the iris and digits datasets for classification and the boston house prices dataset for regression.
Scikit-learn is largely written in Python, with some core algorithms written in Cython to achieve performance. Support vector machines are implemented by a Cython wrapper around LIBSVM; logistic regression and linear support vector machines by a similar wrapper around LIBLINEAR.