UI testing is an important part of quality assurance. Specifically, UI testing refers to the practice of testing front-end components to make sure that they do what they’re supposed to. If a user clicks the Login button, the login modal appears. If they click a link, they’re brought to the appropriate part of the application. With automation platforms, these individual tests can be linked together into workflows and automated. Business-driven development style tests can be created in this fashion. The UI can be tested to see that each individual path that a user may take is functional and that the interface is responding appropriately. Other platforms exist that allow these workflows to be tested on simulated resolutions and devices, ensuring that the user experience is consistent across all possible combinations of browser and device.

API testing lives a layer below UI testing. The UI is fed by these APIs and renders the DOM based upon conditions set by both the user and the developer. These conditions determine the sort of API call that’s made to populate the viewport. When we’re UI Testing, it could be argued that we are indirectly testing the API layer. It’s actually pretty fair to say so. Many of the actions that our UI platform will take will issue API calls. If the DOM rerenders correctly, we can assume to an extent that the API call was successful. The dangerous ground here is the assumption.

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What Is NumPy?

NumPy is a powerful Python library that is primarily used for performing computations on multidimensional arrays. The word NumPy has been derived from two words — Numerical Python. NumPy provides a large set of library functions and operations that help programmers in easily performing numerical computations. These kinds of numerical computations are widely used in tasks like:

  • Machine Learning Models: while writing Machine Learning algorithms, one is supposed to perform various numerical computations on matrices. For instance, matrix multiplication, transposition, addition, etc. NumPy provides an excellent library for easy (in terms of writing code) and fast (in terms of speed) computations. NumPy arrays are used to store both the training data as well as the parameters of the Machine Learning models.
  • Image Processing and Computer Graphics: Images in the computer are represented as multidimensional arrays of numbers. NumPy becomes the most natural choice for the same. NumPy, in fact, provides some excellent library functions for fast manipulation of images. Some examples are mirroring an image, rotating an image by a certain angle, etc.
  • Mathematical tasks: NumPy is quite useful to perform various mathematical tasks like numerical integration, differentiation, interpolation, extrapolation, and many others. As such, it forms a quick Python-based replacement of MATLAB when it comes to Mathematical tasks.

NumPy Installation

The fastest and the easiest way to install NumPy on your machine is to use the following command on the shell: pip install numpy.

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Alibaba Machine Intelligence Technology Laboratory

Established in 2018, the Machine Intelligence Technology Laboratory comprises of a group of outstanding scientists and engineers, with research centers located in Hangzhou, Beijing, Seattle, Silicon Valley, and Singapore. Machine Intelligence Technology Laboratory is Alibaba’s core team responsible for the research and development of artificial intelligence technologies. Relying on Alibaba’s valuable massive data and machine learning/deep learning technologies, the lab has developed image recognition, speech interaction, natural language understanding, intelligent decision-making, and other core artificial intelligence technologies. It fully empowers Alibaba Group’s important businesses such as e-commerce, finance, logistics, social interaction, and entertainment, and also provides outputs to ecosystem partners to jointly build a smart future.

Image Search

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TensorFlow Object Detection is a powerful technology that can recognize different objects in images, including their positions. The trained Object Detection models can be run on mobile and edge devices to execute predictions very quickly. I’ve used this technology to build a demo where Anki Overdrive cars and obstacles are detected via an iOS app. When obstacles are detected, the cars are stopped automatically.

Check out the short video (only 2 mins) for a quick demo.


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If you’ve spent any time job hunting, you’ll no doubt be well aware of how frustrating and hopeless the task can seem at times.

The haphazardous process of getting yourself noticed in a sea of applications that may be prioritized despite carrying less relevance is tricky enough. But then you may find that when you eventually do accept a role, it’s nothing like you were led to believe it would be.


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This GitHub project is a highly interactive graphic demonstration of the features and operations of a generated adversarial network using TensorFow.js. It is based on work done by Minsuk Kahng, Nikhil Thorat, Duen Horng (Polo) Chau, Fernanda B. Viegas, and Martin Wattenberg in their paper: GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation.

Full disclosure: The data sets are small enough and simple enough to demonstrate the technology but clearly are not full-blown image processing examples, which can often consume a lot (CPU/years?) of computer time. But these small examples provide an excellent introduction into what is going on. It takes away the spooky magic…which is a good thing! 


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This article is part of the Key Research Findings from the DZone Guide to Artificial Intelligence: Automating Decision-Making.

Introduction

As part of the research for our 2018 Guide to Artificial Intelligence, we surveyed 403 developers, data scientists, and technologists. From their responses, we’ve created a quick article on the different algorithms available for working with various AI projects, and which one proved more popular.


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Tech companies around the globe have started integrating Artificial Intelligence (AI) into their products for faster processing and substantially reduced manual work. Whether it is search engine results from Google or Siri from Apple, AI has been utilized with perfection by multiple industries to streamline their business practices for better service delivery. However, there are still various tech sub-industries and niches that can take help from AI to finely tune their products and come up with even more customer friendly services. Know Your Customer, also known as KYC, the industry has a lot to benefit from Artificial Intelligence. More and more businesses are being subjected to regulations that require these businesses to carry out full proof KYC verification with the help of an official identity document before a customer is registered. It is high time that AI becomes a cornerstone for KYC software industry for improving the standards of service in this field.

What Is KYC?

As explained above, KYC stands for Know Your Customer. It is a business practice that is conducted before any product is sold or service is utilized by an end-user. The service provider is required to collect comprehensive personal information from their customers. Verifying those credentials is the responsibility of the company that is collecting information from their customers. Most of the times, an official identity document is used to confirm the identity details of a customer. What aspects of a person’s identity are verified, depends on the regulatory guidelines or the nature of business performing a KYC verification.


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La NASA vient de partager le premier cliché réalisé par TESS, le tout nouveau chasseur d’exoplanète de l’agence spatiale.
Source de l’article sur GNT

Demain, vos colis seront peut-être livrés par un robot-véhicule aux petits soins pour vous mais n’oubliant pas pour autant la dimension humaine avec le concept Renault EZ-PRO.
Source de l’article sur GNT