Univ_Techs ::: To Explore Tech

A penetration test, also known as a pen test, is a simulated cyberattacks against your computer system to check for exploitable vulnerabilities. The websites capability is increasing day by day thanks to new web technologies and their integration with other services. The need for security analysts is also increasing. It has been told by experts that the security industry is having a shortage of skilled professionals and this shortage is expected to increase. When we talk about testing something we talk about going through many instances of a single event which has only slight variation in each instance. Manually going through all of it can be time-consuming hence automation is required.  As per the rule of thumb in computer science: repetitive tasks must be automated, so geeks have developed a lot of tools respecting the rule. These tools involve simple scripts as well as all in one testing suites. Most Useful Tools for Web Application Penetration

     ML Kit is a mobile SDK that brings Google's machine learning expertise to Android and iOS apps in a powerful yet easy-to-use package. Whether you're new or experienced in machine learning, you can implement the functionality you need in just a few lines of code. There's no need to have deep knowledge of neural networks or model optimization to get started. On the other hand, if you are an experienced ML developer, ML Kit provides convenient APIs that help you use your custom TensorFlow Lite models in your mobile apps.      At Google I/O'18 developer conference in May, Google introduced ML Kit, a cross-platform suite of machine learning tools for the Firebase mobile development platform. ML Kit uses the Neural Network API on Android devices and is designed to compress and optimize machine learning models for mobile devices.  It brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package. ML Kit makes

     App Maker is available with G Suite Business, Education, and Enterprise editions . Sign in to an account provided by your school or employer and start building apps now.      It’s been a year and a half since Google announced App Maker, its online tool for quickly building and deploying business apps on the web. The company has mostly remained quiet about App Maker ever since and kept it in a private preview mode, but today, it announced that the service is now generally available and open to all developers who want to give it a try.      Access to App Maker comes with any G Suite Business and Enterprise subscription, as well as the G Suite for Education edition. The overall idea here is to help virtually anybody in an organization — including those with little to no coding experience — to build their own line-of-business apps based on data that’s already stored in G Suite, Google’s Cloud SQL database or any other database that supports JDBC or that

Java is a programming language and a platform . It is a high level, robust, secured and object-oriented programming language. Platform : Any hardware or software environment in which a program runs, is known as a platform. Since Java has its own runtime environment (JRE) and API, it is called platform. Java is one of the most popular programming languages and used by millions of developer worldwide. It is a general-purpose programming language that features object-oriented approach. Since its first appearance in 1995, it has always been among top languages in the field despite being much older. Java was originally designed for TV systems i.e.,digital devices such as set-top boxes, televisions etc. But it was quite advanced for it at that point of time. Its creator James Gosling was highly impressed with C, CPP and thus based on it he created this language of his which is still so popular as almost a major portion of developers code in it & also it is one

      Apple announced a new feature for developers called Create ML . It Create machine learning models for use in your app. Create ML is a machine learning framework in Swift which can be used to train the machine learning models using native Apple technologies like Swift, Xcode, and Other Apple frameworks. This means machine learning is possible within Apple ecosystem without any third-party service.  Create ML technology has following features,.. A simple approach for training the model using images, texts, and tabular data Models still can be trained using complex algorithm if you are expert Machine learning models can be created using Xcode Playground, Swift, and native Apple framework The process of creating and training module can be automated using Swift scripts.      You train a model to recognize patterns by showing it representative samples. For example, you can train a model to recognize dogs by showing it lots of images of different dogs. A

Data mining heavily relies on computer processing and a data collection. Data mining tools are used to precisely predict future behaviors and drifts thus allowing businesses to make informed decisions. There are several techniques for data mining and these include looking for incomplete data, dynamic data dashboard, and database analysis.  Programming Languages used for Data mining There are several programming languages used for data mining, the main ones include the following: 1. R      R is a language that dates back to 1997. It was a free substitute to exorbitant statistical software such as SAS or Matlab. R programming language can be used to sift through very complex data sets and to create very sleek graphics that represent numbers in few code lines. This language has the most ideal asset as well as a supportive ecosystem that has been developed around it.      New features and packages were frequently incorporated into its robust functio

          G oogle has invested a large amount of developers time and effort into the capabilities of machine learning.  Finally, the tool was developed by the Brain Team that is TensorFlow.  TensorFlow was orginally designed to be used for Machine Learning and Neural Network applications.          But now, you can use TensorFlow no need to zero  % of installation.  Google announces Colab to do this as easy. Welcome to Colaboratory!      Colaboratory is a Google research project created to help disseminate machine learning education and research. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud.      Colaboratory notebooks are stored in Google Drive and can be shared just as you would with Google Docs or Sheets. Colaboratory is free to use.    How to write Hello, World! in TensorFlow:              import tensorflow as tf                      # import tensorflow .              node = tf.constant(

   In order to tackle with machine learning, you will have to learn few programming languages.      The increasing demand for experts in machine learning in past few years has increased curiosity to know the programming languages which one can use in machine learning. But before discussing, the best programming languages for machine learning you must have brief information about the concept of machine learning. What is machine learning?      In the field of computer science, machine learning is a part of artificial intelligence that provides your computer an ability to learn to improve its performance with data, without being programmed exclusively. In today’s technical world machine learning is one of the fastest progressing concepts on which most of the tech giant companies are heavily investing to improve their merchandise.      In 1959, Arthur Samuel used the words machine learning for the first time to explore the construction of algorithms that c

Pony is an open source, object-oriented, actor-model, capabilities-secure and high-performance programming language.   There are a lot of programming languages out there in real world for different-different tasks. As the technology is evolving, new programming languages are emerging to work with cutting-edge technologies. Google’s GO language and Mozilla’s Rust are the solid examples of new and efficient programming languages which are becoming a lot more popular among the developers. These languages also set an example for developers and enterprises to create or contribute on a new language instead of relying on the old and inefficient programming. The process may take time but make it an open source project often help for faster development. Wallaroo , a distributed data processing framework for building high-performance streaming data applications couldn’t handle the high throughput and low-latency workloads. So the developers had to come up with a pro

     If you are a beginner, check out our articles on ”Machine learning crash course” and “Machine learning specialization course”.      Each of these Frameworks is different from each other and takes much time to learn, during the time of making this list we took care of features other than the basic ones, User base and community & support was one of the most important parameters. Some frameworks are more mathematically oriented, and hence geared more towards statistical than neural networks. Some of them provide a rich set of linear algebra tools; some are mainly focused only on deep learning. 1. TensorFlow TensorFlow an open source software library for data-based programming across a range of tasks, which was developed by Google Brain team and initially released on 9th of November 2015, though the stable release was made available only on 27th of April this year. It is capable of doing regressions, classifications, neural networks, etc. very effect

ML algorithms study is regarded to be the ‘Sexiest job of the 21st century’ as shown in the Harvard Business Review article. For beginners who are eager to study Machine learning basics, here is a great quick guide to the top 10 Machine Learning Algorithms used by ML programmers that you must know. Machine Learning algorithms do not require human intervention, they are able to study data and advance from experience. Learning data entails studying the function that plots input and output and studying the unseen structure from unlabeled data. Ensure you choose the right machine learning task that is appropriate to your problem. Try different Algorithms for every problem to evaluate their performance and then choose the best. Types of Machine Learning Algorithms A basic understanding of varying types of ML Algorithms will enable you to understand better on how algorithms work.   Click Here : To see the types of ML The Top Machine Learning Algorithms  1. Linear Reg