Natural Language: Instructional Materials


SDET/Test Architect Essentials -Road to Full stack QA

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SDET/Test Architect Essentials -Road to Full stack QA by Rahul Shetty Advanced Tutorial to Learn essential skills needed to transform your career from QA Engineer to SDET/Test Architect The one and only Best "Full Stack QA tutorial" which touches up on technical challenges in every phase of Automation by providing smart solutions using latest technologies like Dockers, Jackson API, Jenkin Pipelines, Data Structures using Java Streams, Window batch Scripting, Database readers, GIt and many more!!!!!!!! After Successful course completion, you should be able to apply for any Test Architect /SDET positions or lead the Challenging Automation projects from Scratch Below are in detail Scenarios we are going to cover in this Tutorial Dockerization, integrating Selenium Grid with Docker, Building Json/Xml from database results, Parsing Json into Java objects with Jackson API, Jenkins pipeline Scripting for CI/CD, Dynamically monitoring Server Logs with Java, Windows Batch job scripting, Dataprovider to Excel Integration, Java streams, Lambda expreesions, GIT version control system and many more, What you'll learn Understand and Implement Docker to provide virtualization Environments for Automation Tests Build Json/Xml on fly from JDBC Query results with Jackson API and POJO implementation Build and execute Window batch Scripts for invoking Servers(Selenium/Protractor) Understand Jenkin pipelines scripting for CI/CD Complete knowledge on latest Java Streams and lambda expressions for Interview prep Parsing Json files into Java objects to feed into web Automation tests How to monitor server logs dynamically with java Integrating TestNG Data provider into excel for building robust Datadriven Automation Understanding GIT commands in depth versioncontrol Who this course is for: GET Udemy Discount 95% off SDET/Test Architect Essentials -Road to Full stack QA


Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation

arXiv.org Artificial Intelligence

Data-to-text generation can be conceptually divided into two parts: ordering and structuring the information (planning), and generating fluent language describing the information (realization). Modern neural generation systems conflate these two steps into a single end-to-end differentiable system. We propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization. For training a plan-to-text generator, we present a method for matching reference texts to their corresponding text plans. For inference time, we describe a method for selecting high-quality text plans for new inputs. We implement and evaluate our approach on the WebNLG benchmark. Our results demonstrate that decoupling text planning from neural realization indeed improves the system's reliability and adequacy while maintaining fluent output. We observe improvements both in BLEU scores and in manual evaluations. Another benefit of our approach is the ability to output diverse realizations of the same input, paving the way to explicit control over the generated text structure.


Natural Language Processing(NLP) with Deep Learning in Keras

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Natural Language Processing (NLP) is a hot topic into Machine Learning field. This course is an advanced course of NLP using Deep Learning approach. Before starting this course please read the guidelines of the lesson 2 to have the best experience in this course. This course starts with the configuration and the installation of all resources needed including the installation of Tensor Flow CPU/GPU, Cuda and Keras. You will be able to use your GPU card if you have one, to accelate so fast the processes.


Theorizing from Data by Peter Norvig (Video Lecture)

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Here is a video lecture by Google's Director of Research - Peter Norvig. The full title of this lecture is "Theorizing from Data: Avoiding the Capital Mistake". In 1891 Sir Arthur Conan Doyle said that "it is a capital mistake to theorize before one has data." These words still remain true today. In this talk Peter gives insight into what large amounts of data can do for problems in language understanding, translation and information extraction.


Theorizing from Data by Peter Norvig (Video Lecture)

#artificialintelligence

Here is a video lecture by Google's Director of Research - Peter Norvig. The full title of this lecture is "Theorizing from Data: Avoiding the Capital Mistake". In 1891 Sir Arthur Conan Doyle said that "it is a capital mistake to theorize before one has data." These words still remain true today. In this talk Peter gives insight into what large amounts of data can do for problems in language understanding, translation and information extraction.


Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds

arXiv.org Artificial Intelligence

Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge. These two sources have historically shaped the kinds of questions that are asked over these sources, and the methods developed to answer them. In this work, we look towards a practical use-case of QA over user-instructed knowledge that uniquely combines elements of both structured QA over knowledge bases, and unstructured QA over narrative, introducing the task of multi-relational QA over personal narrative. As a first step towards this goal, we make three key contributions: (i) we generate and release TextWorldsQA, a set of five diverse datasets, where each dataset contains dynamic narrative that describes entities and relations in a simulated world, paired with variably compositional questions over that knowledge, (ii) we perform a thorough evaluation and analysis of several state-of-the-art QA models and their variants at this task, and (iii) we release a lightweight Python-based framework we call TextWorlds for easily generating arbitrary additional worlds and narrative, with the goal of allowing the community to create and share a growing collection of diverse worlds as a test-bed for this task.


Neo4j Graph Database for Analytics and Data Science

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Use coupon code ALMOSTFREE and get FLAT 95% discount Learn how to organize your data with the popular Neo4j graph database in this Neo4J database tutorial!! Search engines and social media platforms have propelled graph databases into the lime light. While traditional relational databases are still popular among many companies, graph databases are slowly climbing the ranks as a go to database for many complex structures. Databases play an important role when it comes to storing and fetching large amounts of data. Data is often a huge mess on the internet, which needs to be meticulously sorted into sections and sub-sections to make it easier for analyzing. Data is in raw form is useless for individuals and companies alike, until it is sorted and provides the user with information or it can specifically answer the user's question.


The Next Frontier: Healthcare Artificial Intelligence Consulting

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There is an undeniable truth that Artificial Intelligence, which we will refer to simply as AI, is the next frontier for the healthcare industry. Several sources have already pegged the market to be worth $36.1 billion by 2025. For those of you who like simple language; the way AI works is by having it developed through machine learning, natural language processing, and deep learning. This process is controlled by programmers, who in a lot of cases are independent contractors. Regulatory frameworks will soon be created to govern this new boom, with consulting and online training courses becoming the next cash cows milking this industry for profits.


GDS Academy launches new course: Introduction to artificial intelligence in government

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Starting in March 2019, the course will take you on a tour of how automation can revolutionise work in government from robotic process automation via machine learning to natural language processing and deep learning, covering ethics in emerging technology and how to get started in your organisation.


Natural Language Processing with Python and NLTK

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Natural Language Processing (NLP) is a hot topic into the Machine Learning field. This course is focused in practical approach with many examples and developing functional applications. This course starts explaining you, how to get the basic tools for coding and also making a review of the main machine learning concepts and algorithms. After that this course offers you a complete explanation of the main tools in NLP such as: Text Data Assemble, Text Data Preprocessing, Text Data Visualization, Model Building and finally developing NLP applications. In this course you will find a concise review of the theory with graphical explanations and for coding it uses Python language and NLTK library.