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Deep learning can be used to improve NLG by utilizing machine learning algorithms to analyze and process large amounts of text data. This data can then be used to generate more natural language responses and output. Deep learning models such as recurrent neural networks and long short-term memory networks can be used to generate more accurate and natural language outputs that are more context-aware. These models can also help improve the accuracy of language processing tasks such as language translation and summarization. Additionally, deep learning can also be used to create more sophisticated dialogue systems that are able to respond more intelligently to user input.
Deep learning can be used to improve recommendation engines by leveraging large amount of data to better understand user preferences. This data can be used to create more complex models that can better identify user interests and generate more accurate recommendations. Deep learning algorithms can also be used to identify patterns in user behavior, allowing the recommendation engine to better understand user preferences and make better predictions. Additionally, deep learning can be used to identify relationships between items, allowing the recommendation engine to suggest items that may be of interest to the user that they may not have seen before.
Deep learning can be used to improve natural language dialog systems by providing better understanding of the conversation. Deep learning algorithms allow the system to interpret and learn from the conversation to better understand the user's intent and provide answers that are more accurate and relevant. Deep learning can also be used to interpret and respond to sentiment, allowing the system to respond more effectively to the user's feelings and emotions. Furthermore, deep learning can be used to create more natural sounding dialogue and better interpret non-verbal cues. All of this can help create a more natural and engaging dialogue experience for the user.
Deep learning can be used to improve intelligent virtual agents by allowing them to better recognize patterns and interpret user input. Deep learning techniques such as natural language processing (NLP) can be used to allow virtual agents to understand natural language and better respond to user queries. Additionally, deep learning can be used to improve the accuracy of predictions and recommendations given by the virtual agents. For example, using deep learning techniques, virtual agents can better recognize user preferences, learn from past interactions, and predict user needs and behaviors.
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First touchpoint for customer Initial handling of all customer tickets Track to closure of customer tickets by assisting the responsible teams System software and AWS/Azure infrastructure L1/L2 support Newgen solution / application L1/L2 support Responsib
Experience: 0 to 4 years Qualification:B.SC, B.Tech/BE/MCA Skills in one or more of JavaScript,CSS, Web application framework viz. Sencha EXT JS, JQuery etc., Delphi,C,C++,or Java..net,testing Cloud Administrator-managing Windows based Servers
Developing and deploying new applications on the windows azure PAAS platform using C#, .net core . Participation in the creation and management of databases like SQL server and MySQL Understanding of data storage technology (RDBMS, NO SQL). Manage applica
Experience of Dev Ops technologies, architectures and processes for 3 yrs Cloud Infrastructures Solutions: AWS EC2 ECS, S3 Cloudfront, RDS, Spot Instances, VPC, IAM, Security Groups, ELB etc), GCP, CI/CD Jenkins Containerization: Docker, Kubernetes System
Must have good knowledge of Google Cloud (GCP), Good To Have- AWS and. Azure Cloud automation, with overall cloud computing experience. Good knowledge of Windows Server and IIS (Internet Information Services). Good knowledge of .NET applications (ASP.Net,
Good Knowledge in both Manual Testing and Automation Testing,Strong experience in writing test scenarios and test cases Strong knowledge on Selenium, Appium, Microsoft SQL and Jmeter Adept in functional testing and reporting defects
Design, execute and report software tests, Review business / software requirements and provide inputs. Prepare test cases, test scripts and test data., Execute tests (manual / automated). Report defects and assist in their understanding., Analyse test re
Good Knowledge in both Manual Testing and Automation Testing,Strong experience in writing test scenarios and test cases Strong knowledge on Selenium, Appium, Microsoft SQL and Jmeter Adept in functional testing and reporting defects
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