What is Julia Language? Julia is a high-level, high-performance dynamic programming language for numerical computing.
It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. Julia’s Base library, largely written in Julia itself, also integrates mature, best-of-breed open source C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing. In addition, the Julia developer community is contributing a number of external packages through Julia’s built-in package manager at a rapid pace. IJulia, a collaboration between the Jupyter and Julia communities, provides a powerful browser-based graphical notebook interface to Julia.
Julia programs are organized around multiple dispatch; by defining functions and overloading them for different combinations of argument types, which can also be user-defined.
Multiple dispatch: providing ability to define function behavior across many combinations of argument types
Dynamic type system: types for documentation, optimization, and dispatch
Good performance, approaching that of statically-compiled languages like C
Built-in package manager
Lisp-like macros and other metaprogramming facilities
Call Python functions: use the PyCall package
Call C functions directly: no wrappers or special APIs
Powerful shell-like capabilities for managing other processes
Designed for parallelism and distributed computation
Coroutines: lightweight “green” threading
User-defined types are as fast and compact as built-ins
Automatic generation of efficient, specialized code for different argument types
Elegant and extensible conversions and promotions for numeric and other types
Efficient support for Unicode, including but not limited to UTF-8
MIT licensed: free and open source
Video for Julia Language https://www.youtube.com/watch?v=PuAIaDRDDQA
Delphi is both an object oriented programming language (OOP) and an Integrated Development Environment (IDE). Published by the Embarcadero company (formerly CodeGear and more formerly Borland), Delphi is an alternative to language such as Visual Basic offering development with both rapidity and good quality.
Delphi includes the RunTime Library (RTL) that provides basic functionality across all the platforms. For Windows it provides the Visual Component Library (VCL), and for cross platform development it includes FireMonkey (FMX).
Delphi includes a code editor, a visual designer, an integrated debugger, a source code control component, and support for third-party plugins. The code editor features Code Insight (code completion), Error Insight (real-time error-checking), and refactoring.
The visual forms designer has traditionally used Visual Component Library (VCL) for native Windows development, but the FireMonkey (FMX) platform was later added for cross-platform development. Database support in Delphi is very strong. A Delphi project of a million lines to compile in a few seconds – one benchmark gave 170,000 lines per second.
It provides interfaces for the programmer to build an application using the Extensible Markup Language (XML), Extensible Stylesheet Language (XSL), Simple Object Access Protocol (SOAP), and Web Services Description Language (WSDL).
ThingSpeak is an open-source Internet of Things (IoT) application and API to store and retrieve data from things using the HTTP protocol over the Internet or via a Local Area Network.ThingSpeak was originally launched by ioBridge in 2010 as a service in support of IoT applications. ThingSpeak™ is an IoT analytics platform service that allows you to aggregate, visualize and analyze live data streams in the cloud.
ThingSpeak provides instant visualizations of data posted by your devices to ThingSpeak. With the ability to execute MATLAB® code in ThingSpeak you can perform online analysis and processing of the data as it comes in. ThingSpeak is often used for prototyping and proof of concept IoT systems that require analytics.
ThingSpeak allows you to aggregate, visualize and analyze live data streams in the cloud.
Some of the key capabilities of ThingSpeak include the ability to
Easily configure devices to send data to ThingSpeak using popular IoT protocols.
Visualize your sensor data in real-time.
Aggregate data on-demand from third-party sources.
Use the power of MATLAB to make sense of your IoT data.
Run your IoT analytics automatically based on schedules or events.
Prototype and build IoT systems without setting up servers or developing web software.
Automatically act on your data and communicate using third-party services like Twilio® or Twitter®.
Minikube is a tool that makes it easy to run Kubernetes locally. Minikube runs a single-node Kubernetes cluster inside a VM on your laptop for users looking to try out Kubernetes or develop with it day-to-day.
Minikube supports Kubernetes features such as:
ConfigMaps and Secrets
Container Runtime: Docker, rkt, CRI-O and containerd
Enabling CNI (Container Network Interface)
When using a single VM of Kubernetes, it’s really handy to reuse the Minikube’s built-in Docker daemon; as this means you don’t have to build a docker registry on your host machine and push the image into it -
We can just build inside the same docker daemon as minikube which speeds up local experiments. Just make sure you tag your Docker image with something other than ‘latest’ and use that tag while you pull the image. Otherwise, if you do not specify version of your image,
it will be assumed as :latest, with pull image policy of Always correspondingly, which may eventually result in ErrImagePull as you may not have any versions of your Docker image out there in the default docker registry (usually DockerHub) yet.
Polymer provides a number of features over vanilla Web Components:
Simplified way of creating custom elements
Both One-way and Two-way data binding
Conditional and repeat templates
Polymer.js places a hefty set of requirements on the browser, relying on a number of technologies that are in still in the process of standardization (by W3C) and are not yet present in today’s browsers.
Examples include the shadow dom, template elements, custom elements, HTML imports, mutation observers, model-driven views, pointer events, and web animations. These are marvelous technologies, but at least as of now, that are yet-to-come to modern browsers.
The Polymer strategy is to have front-end developers leverage these leading-edge, still-to-come, browser-based technologies, which are currently in the process of standardization (by W3C), as they become available.
The recommended polyfills are designed in such a way that (at least theoretically) will be seamless to replace once the native browser versions of these capabilities become available.
Video for Polymer.Js https://www.youtube.com/watch?v=tvafAyxkuVk
Start quickly with built-in navigators that deliver a seamless out-of-the-box experience.
2) Components built for iOS and Android
Platform-specific look-and-feel with smooth animations and gestures.
3) Completely customizable
4) Extensible platform
React Navigation is extensible at every layer— you can write your own navigators or even replace the user-facing API.
MLlib (Spark) is Apache Spark’s machine learning library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.
Regression: generalized linear regression, survival regression,...
Decision trees, random forests, and gradient-boosted trees
Recommendation: alternating least squares (ALS)
Clustering: K-means, Gaussian mixtures (GMMs),...
Topic modeling: latent Dirichlet allocation (LDA)
Frequent itemsets, association rules, and sequential pattern mining
Spark revolves around the concept of a resilient distributed dataset (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are two ways to create RDDs: parallelizing an existing collection in your driver program, or referencing a dataset in an external storage system, such as a shared filesystem, HDFS, HBase, or any data source offering a Hadoop InputFormat.