Confluence is project management tool which provide you best platform to create, organize, and discuss work with your team. Confluence makes team to spend less time hunting things down and more time making things happen. Organize your work, create documents, and discuss everything in one place.
Below operation we can perform under Jira confluence tool:
Create anything your team needs - meeting notes, product requirements, knowledge base articles - on the web so everyone can contribute.
Give feedback on any Confluence page or file with inline and pinned comments. No more ridiculous file_names_with_dates.doc or messy track changes.
Capture all the information that's scattered among email inboxes and countless apps in the same place.
Give every team, project, or department its own space to store work. Confluence keeps everything organized and accessible.
Confluence gives you the power to create anything - meeting notes, project plans, product requirements, etc. – thanks to a simple, but powerful editor.
You work with files every day – images, PDFs, spreadsheets, and presentations. You can give feedback directly on your files in Confluence, and it keeps tracks of versions automatically, so you're always working on the right one.
Create reports and charts
Reporting on information stored in JIRA is simple in Confluence. In addition to the JIRA Issues Macro, you can use the JIRA Report blueprint or JIRA Chart macro to show information from your JIRA application visually. It's the best way to give your stakeholders a snapshot of your team or project's progress.
Use the JIRA report blueprint to create a Change Log or Status report.
Use the JIRA Chart Micros to display data as a chart, including pie charts, created vs resolved, and two dimensional charts.
Use JIRA Gadgets to display detailed JIRA reports and charts on pages.
EOS is a blockchain platform for the development of decentralized applications (dapps), similar to Ethereum in function. It provides a complete operating system for decentralized applications focused on the web with services like user authentication, cloud storage, and server hosting.
EOSIO is a free, open-source blockchain software protocol that provides developers and entrepreneurs with a platform on which to build, deploy and run high-performing decentralized applications (DAPPs)
EOSIO based blockchains execute user-generated applications and code using WebAssembly (WASM). WASM is an emerging web standard with widespread support of Google, Microsoft, Apple, and industry leading companies.
At the moment the most mature toolchain for building applications that compile to WASM is clang/llvm with their C/C++ compiler. For best compatibility, it is recommended that you use the EOSIO toolchain.
Other toolchains in development by 3rd parties include: Rust, Python, and Solidity. While these other languages may appear simpler, their performance will likely impact the scale of application you can build. We expect that C++ will be the best language for developing high-performance and secure smart contracts and plan to use C++ for the foreseeable future.
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
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.
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.
MLlib is Spark’s scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives, as outlined below:
Classification and regression
Feature extraction and transformation
Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface centered on the RDD abstraction This interface mirrors a functional/higher-order model of programming: a "driver" program invokes parallel operations such as map, filter or reduce on an RDD by passing a function to Spark, which then schedules the function's execution in parallel on the cluster.
These operations, and additional ones such as joins, take RDDs as input and produce new RDDs. RDDs are immutable and their operations are lazy; fault-tolerance is achieved by keeping track of the "lineage" of each RDD so that it can be reconstructed in the case of data loss. RDDs can contain any type of Python, Java, or Scala objects.