Bhashini (NLTM) Tools
•Download as PPTX, PDF•
0 likes•675 views
- Bhashini Mission - Need for Digital Infrastructure - NLTM Architecture - Datasets & Models - BhashaDaan - ULCA - Contributing Datasets & Models to ULCA - Roadmap
Report
Share
Report
Share
Recommended
NTLM - Open Source Language AI Tools
- Need for AI Data Platforms/tools
- Data Collection Ecosystem
- Challenges for Dataset Contributors
- Shoonya
- Chitralekha
- Anuvaad
Natural Language Processing
Natural Language Processing (NLP) is a subfield of artificial intelligence that aims to help computers understand human language. NLP involves analyzing text at different levels, including morphology, syntax, semantics, discourse, and pragmatics. The goal is to map language to meaning by breaking down sentences into syntactic structures and assigning semantic representations based on context. Key steps include part-of-speech tagging, parsing sentences into trees, resolving references between sentences, and determining intended meaning and appropriate actions. Together, these allow computers to interpret and respond to natural human language.
Recommending for the World
The Netflix experience is driven by a number of Machine Learning algorithms: personalized ranking, page generation, search, similarity, ratings, etc. On the 6th of January, we simultaneously launched Netflix in 130 new countries around the world, which brings the total to over 190 countries. Preparing for such a rapid expansion while ensuring each algorithm was ready to work seamlessly created new challenges for our recommendation and search teams. In this post, we highlight the four most interesting challenges we’ve encountered in making our algorithms operate globally and, most importantly, how this improved our ability to connect members worldwide with stories they'll love.
Semantic Web, Ontology, and Ontology Learning: Introduction
(1) The document discusses the Semantic Web, ontologies, and ontology learning. It defines the Semantic Web as an extension of the current web that gives information well-defined meaning. (2) Ontologies are formal specifications of concepts and relations that provide shared meanings between machines and humans. (3) Ontology learning is the automatic or semi-automatic process of extracting ontological concepts and relations from text to build or enrich ontologies. The document outlines methods for ontology learning and its applications.
Programming languages
This document discusses several programming languages including BASIC, FORTRAN, Pascal, C, Java, and HTML. It provides brief descriptions of each language, noting that BASIC was developed as a teaching aid, FORTRAN was used for scientific applications, Pascal supports structured programming, C was developed at Bell Labs, Java is a general purpose object-oriented language, and HTML is the standard markup language for web pages. The document also lists some common HTML tags like title, paragraph, and lists and describes their basic functions.
Python in real world.
a presentation about python programming language made and presented by me in a lecture to show the importance of python in the real world to my colleagues
Knowledge Garden Overview
Overview of the Knowledge Garden concept, its philosophy, topology, and large concepts. Organized in SRI International's NABC format
Nlp
The document discusses natural language and natural language processing (NLP). It defines natural language as languages used for everyday communication like English, Japanese, and Swahili. NLP is concerned with enabling computers to understand and interpret natural languages. The summary explains that NLP involves morphological, syntactic, semantic, and pragmatic analysis of text to extract meaning and understand context. The goal of NLP is to allow humans to communicate with computers using their own language.
Recommended
NTLM - Open Source Language AI Tools
- Need for AI Data Platforms/tools
- Data Collection Ecosystem
- Challenges for Dataset Contributors
- Shoonya
- Chitralekha
- Anuvaad
Natural Language Processing
Natural Language Processing (NLP) is a subfield of artificial intelligence that aims to help computers understand human language. NLP involves analyzing text at different levels, including morphology, syntax, semantics, discourse, and pragmatics. The goal is to map language to meaning by breaking down sentences into syntactic structures and assigning semantic representations based on context. Key steps include part-of-speech tagging, parsing sentences into trees, resolving references between sentences, and determining intended meaning and appropriate actions. Together, these allow computers to interpret and respond to natural human language.
Recommending for the World
The Netflix experience is driven by a number of Machine Learning algorithms: personalized ranking, page generation, search, similarity, ratings, etc. On the 6th of January, we simultaneously launched Netflix in 130 new countries around the world, which brings the total to over 190 countries. Preparing for such a rapid expansion while ensuring each algorithm was ready to work seamlessly created new challenges for our recommendation and search teams. In this post, we highlight the four most interesting challenges we’ve encountered in making our algorithms operate globally and, most importantly, how this improved our ability to connect members worldwide with stories they'll love.
Semantic Web, Ontology, and Ontology Learning: Introduction
(1) The document discusses the Semantic Web, ontologies, and ontology learning. It defines the Semantic Web as an extension of the current web that gives information well-defined meaning. (2) Ontologies are formal specifications of concepts and relations that provide shared meanings between machines and humans. (3) Ontology learning is the automatic or semi-automatic process of extracting ontological concepts and relations from text to build or enrich ontologies. The document outlines methods for ontology learning and its applications.
Programming languages
This document discusses several programming languages including BASIC, FORTRAN, Pascal, C, Java, and HTML. It provides brief descriptions of each language, noting that BASIC was developed as a teaching aid, FORTRAN was used for scientific applications, Pascal supports structured programming, C was developed at Bell Labs, Java is a general purpose object-oriented language, and HTML is the standard markup language for web pages. The document also lists some common HTML tags like title, paragraph, and lists and describes their basic functions.
Python in real world.
a presentation about python programming language made and presented by me in a lecture to show the importance of python in the real world to my colleagues
Knowledge Garden Overview
Overview of the Knowledge Garden concept, its philosophy, topology, and large concepts. Organized in SRI International's NABC format
Nlp
The document discusses natural language and natural language processing (NLP). It defines natural language as languages used for everyday communication like English, Japanese, and Swahili. NLP is concerned with enabling computers to understand and interpret natural languages. The summary explains that NLP involves morphological, syntactic, semantic, and pragmatic analysis of text to extract meaning and understand context. The goal of NLP is to allow humans to communicate with computers using their own language.
Natural Language Processing
This document provides an introduction and overview of natural language processing (NLP). It discusses what NLP is, how machines can process human language, the history and importance of NLP, and the typical components and processes involved, including morphological/lexical analysis, syntactic analysis, semantic analysis, discourse integration, and pragmatic analysis. The document also compares natural language to computer languages, discusses the future of NLP being linked to advances in artificial intelligence, and summarizes that NLP involves disambiguation at various linguistic levels through statistical learning methods.
Python and Machine Learning
The document provides an overview of various Python machine learning libraries and tools, including Orange, MDP, PyMC, PyML, hcluster, NLTK, mlpy, LIBSVM, PyEvolve, FANN, Theano, PyBrain, Shogun, ffnet. For each library, it gives information on the homepage, dependencies, installation/source options, key developers and details. It also discusses machine learning and Python in general terms, noting the large amount of activity but also varying documentation quality and lack of packaging.
NLP
This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
Evolution of Computer Languages
All the Applications, Web pages, Programming Codes are written in a specific computer language. It’s interesting to see where computer languages on track and how they have evolved over time. There are now a series of computer languages to choose from and billions lines of code. Check out the Slides to see the computer language timeline and about code along the way.
Nlp presentation
This document provides an overview of a course on trends and research applications in natural language processing (NLP). It begins with introducing the goals of the course, which are to understand interesting NLP tasks and novel projects through a research-oriented webinar. The document then covers various NLP topics like question answering, machine translation, sentiment analysis, natural language generation applications, and challenges in NLP like grounded language and embodied language. It also provides tips for aspiring NLP researchers.
Language translator
This document discusses language translation and provides an overview of a language translation tool. It begins with an introduction that defines translation and its objectives. It then discusses why translation is necessary in different contexts like education, business, and media. The document outlines the hardware, software, and development tools required for the language translation tool, including using Python and Visual Studio Code. It describes the methodology used in the tool, which utilizes the Googletrans library to implement Google Translate API. The modes of the translation tool include writing text, processing, output, and listening. The document concludes with discussing the future of translation and the benefits of language translators.
Python Anaconda Tutorial | Edureka
YouTube Link: https://youtu.be/beh7GE4FdnM
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Python Anaconda Tutorial' will help you understand how you can work on anaconda using python with installation and setup including use case consisting of python fundamentals and data analysis. Following are the topics discussed:
Introduction to Anaconda
Installation And Setup
How To Install Libraries?
Anaconda Navigator
Use Case - Python Fundamentals
Use Case - Data Analysis
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)
This document provides an overview of transformer seq2seq models, including their concepts, trends, and limitations. It discusses how transformer models have replaced RNNs for seq2seq tasks due to being more parallelizable and effective at modeling long-term dependencies. Popular seq2seq models like T5, BART, and Pegasus are introduced. The document reviews common pretraining objectives for seq2seq models and current trends in larger model sizes, task-specific pretraining, and long-range modeling techniques. Limitations discussed include the need for grounded representations and efficient generation for seq2seq models.
Natural Language Processing
Natural Language Processing(NLP) is a subset Of AI.It is the ability of a computer program to understand human language as it is spoken.
Contents
What Is NLP?
Why NLP?
Levels In NLP
Components Of NLP
Approaches To NLP
Stages In NLP
NLTK
Setting Up NLP Environment
Some Applications Of NLP
Introduction to Natural Language Processing (NLP)
This document introduces natural language processing (NLP) and describes how it works. NLP involves using AI techniques like machine learning to understand and generate human language. It converts unstructured text into structured knowledge. Key NLP tasks include entity recognition, topic analysis, sentiment analysis, and classification. Common applications are spellcheckers, recommendation systems, voice assistants, search engines, and language translation. An example project called Switch uses NLP techniques on Twitter data to build a job search engine. It extracts entities, classifies tweets, and provides a website for users to search relevant job postings.
NLP
Natural language processing (NLP) is introduced, including its definition, common steps like morphological analysis and syntactic analysis, and applications like information extraction and machine translation. Statistical NLP aims to perform statistical inference for NLP tasks. Real-world applications of NLP are discussed, such as automatic summarization, information retrieval, question answering and speech recognition. A demo of a free NLP application is presented at the end.
Discover AI with Microsoft Azure
This document provides an agenda for an AI workshop that covers various Microsoft AI technologies including computer vision, speech, and language. The agenda includes discussions on Microsoft's breakthroughs in computer vision by winning ImageNet competitions five years in a row. It also covers Microsoft's speech breakthroughs and ongoing momentum. The bulk of the agenda focuses on demonstrating various Microsoft Cognitive Services like Vision, Speech, Language, Translator, LUIS, and Bing APIs. It provides examples of calling the Computer Vision and Translator APIs and summarizes several Cognitive Services like Text Analytics, Spell Check, and Language Understanding. The document aims to educate attendees on Microsoft's broad portfolio of AI services and tools.
Natural Language Processing
Natural language processing (NLP) involves building models to understand human language through automated generation and understanding of text and speech. It is an interdisciplinary field that uses techniques from artificial intelligence, linguistics, and statistics. NLP has applications like machine translation, sentiment analysis, and summarization. There are two main approaches: statistical NLP which uses machine learning on large datasets, and linguistic approaches which utilize structured linguistic resources like lexicons. Key NLP tasks include part-of-speech tagging, parsing, named entity recognition, and more.
Natural language processing
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Build and Distributing SDK Add-Ons
Walkthrough of how to use SDK Add-ons to distribute the features of your Android-based custom device to developers.
Topic Modeling - NLP
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
A tutorial on Machine Translation
This document provides an introduction to machine translation and different approaches to machine translation. It discusses the history of machine translation, beginning in the 1950s. It then describes four main approaches to machine translation: direct machine translation, rule-based machine translation, corpus-based machine translation, and knowledge-based machine translation. For each approach, it provides a brief overview and example. It focuses in more depth on direct machine translation and rule-based machine translation, explaining their process and limitations.
Lecture 3 basic syntax and semantics
This document discusses Python syntax and semantics. It introduces key concepts like statements, modules, comments, whitespace, indentation, tokens, expressions, and interpreter errors. It also discusses the difference between semantics, which is the meaning of a program, and syntax, which specifies the algorithm using the programming language. An example program is provided and explained to demonstrate various syntax elements.
Advanced Natural Language Processing with Apache Spark NLP
This document provides an overview of Spark NLP, an open-source library for natural language processing (NLP). It introduces Spark NLP and discusses its state-of-the-art accuracy on NLP tasks like named entity recognition and text classification. It also covers Spark NLP's speed, scalability, and ease of use. Examples are given of training NLP models with Spark NLP for tasks like part-of-speech tagging, named entity recognition, and text classification.
Recurrent Neural Networks for Recommendations and Personalization with Nick P...
In the last few years, RNNs have achieved significant success in modeling time series and sequence data, in particular within the speech, language, and text domains. Recently, these techniques have been begun to be applied to session-based recommendation tasks, with very promising results.
This talk explores the latest research advances in this domain, as well as practical applications. I will provide an overview of RNNs, covering common architectures and applications, before diving deeper into RNNs for session-based recommendations. I will pay particular attention to the challenges inherent in common personalization tasks and the specific adjustments to models and optimization techniques required for success.
Dhruva - Deploying models at scale.pptx
The document discusses Dhruva, an open-source platform for deploying language AI services at scale. It provides a standardized interface for deploying and collaborating on open AI models. Dhruva aims to optimize models for efficient deployment and provide features like auto-scaling, usage monitoring, and supporting multiple AI tasks. The document demonstrates Dhruva's frontend and describes its technical architecture using technologies like FastAPI, MongoDB, Redis, and Nvidia Triton for optimized model deployment.
Web Annotations – A Game Changer for Language Technology?
Georg Rehm, Felix Sasaki, and Aljoscha Burchardt. Web Annotations - A Game Changer for Language Technologies? I Annotate 2016, Berlin, Germany, May 2016. May 19/20, 2016.
More Related Content
What's hot
Natural Language Processing
This document provides an introduction and overview of natural language processing (NLP). It discusses what NLP is, how machines can process human language, the history and importance of NLP, and the typical components and processes involved, including morphological/lexical analysis, syntactic analysis, semantic analysis, discourse integration, and pragmatic analysis. The document also compares natural language to computer languages, discusses the future of NLP being linked to advances in artificial intelligence, and summarizes that NLP involves disambiguation at various linguistic levels through statistical learning methods.
Python and Machine Learning
The document provides an overview of various Python machine learning libraries and tools, including Orange, MDP, PyMC, PyML, hcluster, NLTK, mlpy, LIBSVM, PyEvolve, FANN, Theano, PyBrain, Shogun, ffnet. For each library, it gives information on the homepage, dependencies, installation/source options, key developers and details. It also discusses machine learning and Python in general terms, noting the large amount of activity but also varying documentation quality and lack of packaging.
NLP
This document provides an overview of natural language processing (NLP). It discusses topics like natural language understanding, text categorization, syntactic analysis including parsing and part-of-speech tagging, semantic analysis, and pragmatic analysis. It also covers corpus-based statistical approaches to NLP, measuring performance, and supervised learning methods. The document outlines challenges in NLP like ambiguity and knowledge representation.
Evolution of Computer Languages
All the Applications, Web pages, Programming Codes are written in a specific computer language. It’s interesting to see where computer languages on track and how they have evolved over time. There are now a series of computer languages to choose from and billions lines of code. Check out the Slides to see the computer language timeline and about code along the way.
Nlp presentation
This document provides an overview of a course on trends and research applications in natural language processing (NLP). It begins with introducing the goals of the course, which are to understand interesting NLP tasks and novel projects through a research-oriented webinar. The document then covers various NLP topics like question answering, machine translation, sentiment analysis, natural language generation applications, and challenges in NLP like grounded language and embodied language. It also provides tips for aspiring NLP researchers.
Language translator
This document discusses language translation and provides an overview of a language translation tool. It begins with an introduction that defines translation and its objectives. It then discusses why translation is necessary in different contexts like education, business, and media. The document outlines the hardware, software, and development tools required for the language translation tool, including using Python and Visual Studio Code. It describes the methodology used in the tool, which utilizes the Googletrans library to implement Google Translate API. The modes of the translation tool include writing text, processing, output, and listening. The document concludes with discussing the future of translation and the benefits of language translators.
Python Anaconda Tutorial | Edureka
YouTube Link: https://youtu.be/beh7GE4FdnM
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'Python Anaconda Tutorial' will help you understand how you can work on anaconda using python with installation and setup including use case consisting of python fundamentals and data analysis. Following are the topics discussed:
Introduction to Anaconda
Installation And Setup
How To Install Libraries?
Anaconda Navigator
Use Case - Python Fundamentals
Use Case - Data Analysis
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)
This document provides an overview of transformer seq2seq models, including their concepts, trends, and limitations. It discusses how transformer models have replaced RNNs for seq2seq tasks due to being more parallelizable and effective at modeling long-term dependencies. Popular seq2seq models like T5, BART, and Pegasus are introduced. The document reviews common pretraining objectives for seq2seq models and current trends in larger model sizes, task-specific pretraining, and long-range modeling techniques. Limitations discussed include the need for grounded representations and efficient generation for seq2seq models.
Natural Language Processing
Natural Language Processing(NLP) is a subset Of AI.It is the ability of a computer program to understand human language as it is spoken.
Contents
What Is NLP?
Why NLP?
Levels In NLP
Components Of NLP
Approaches To NLP
Stages In NLP
NLTK
Setting Up NLP Environment
Some Applications Of NLP
Introduction to Natural Language Processing (NLP)
This document introduces natural language processing (NLP) and describes how it works. NLP involves using AI techniques like machine learning to understand and generate human language. It converts unstructured text into structured knowledge. Key NLP tasks include entity recognition, topic analysis, sentiment analysis, and classification. Common applications are spellcheckers, recommendation systems, voice assistants, search engines, and language translation. An example project called Switch uses NLP techniques on Twitter data to build a job search engine. It extracts entities, classifies tweets, and provides a website for users to search relevant job postings.
NLP
Natural language processing (NLP) is introduced, including its definition, common steps like morphological analysis and syntactic analysis, and applications like information extraction and machine translation. Statistical NLP aims to perform statistical inference for NLP tasks. Real-world applications of NLP are discussed, such as automatic summarization, information retrieval, question answering and speech recognition. A demo of a free NLP application is presented at the end.
Discover AI with Microsoft Azure
This document provides an agenda for an AI workshop that covers various Microsoft AI technologies including computer vision, speech, and language. The agenda includes discussions on Microsoft's breakthroughs in computer vision by winning ImageNet competitions five years in a row. It also covers Microsoft's speech breakthroughs and ongoing momentum. The bulk of the agenda focuses on demonstrating various Microsoft Cognitive Services like Vision, Speech, Language, Translator, LUIS, and Bing APIs. It provides examples of calling the Computer Vision and Translator APIs and summarizes several Cognitive Services like Text Analytics, Spell Check, and Language Understanding. The document aims to educate attendees on Microsoft's broad portfolio of AI services and tools.
Natural Language Processing
Natural language processing (NLP) involves building models to understand human language through automated generation and understanding of text and speech. It is an interdisciplinary field that uses techniques from artificial intelligence, linguistics, and statistics. NLP has applications like machine translation, sentiment analysis, and summarization. There are two main approaches: statistical NLP which uses machine learning on large datasets, and linguistic approaches which utilize structured linguistic resources like lexicons. Key NLP tasks include part-of-speech tagging, parsing, named entity recognition, and more.
Natural language processing
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Build and Distributing SDK Add-Ons
Walkthrough of how to use SDK Add-ons to distribute the features of your Android-based custom device to developers.
Topic Modeling - NLP
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
A tutorial on Machine Translation
This document provides an introduction to machine translation and different approaches to machine translation. It discusses the history of machine translation, beginning in the 1950s. It then describes four main approaches to machine translation: direct machine translation, rule-based machine translation, corpus-based machine translation, and knowledge-based machine translation. For each approach, it provides a brief overview and example. It focuses in more depth on direct machine translation and rule-based machine translation, explaining their process and limitations.
Lecture 3 basic syntax and semantics
This document discusses Python syntax and semantics. It introduces key concepts like statements, modules, comments, whitespace, indentation, tokens, expressions, and interpreter errors. It also discusses the difference between semantics, which is the meaning of a program, and syntax, which specifies the algorithm using the programming language. An example program is provided and explained to demonstrate various syntax elements.
Advanced Natural Language Processing with Apache Spark NLP
This document provides an overview of Spark NLP, an open-source library for natural language processing (NLP). It introduces Spark NLP and discusses its state-of-the-art accuracy on NLP tasks like named entity recognition and text classification. It also covers Spark NLP's speed, scalability, and ease of use. Examples are given of training NLP models with Spark NLP for tasks like part-of-speech tagging, named entity recognition, and text classification.
Recurrent Neural Networks for Recommendations and Personalization with Nick P...
In the last few years, RNNs have achieved significant success in modeling time series and sequence data, in particular within the speech, language, and text domains. Recently, these techniques have been begun to be applied to session-based recommendation tasks, with very promising results.
This talk explores the latest research advances in this domain, as well as practical applications. I will provide an overview of RNNs, covering common architectures and applications, before diving deeper into RNNs for session-based recommendations. I will pay particular attention to the challenges inherent in common personalization tasks and the specific adjustments to models and optimization techniques required for success.
What's hot (20)
Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)
Transformer Seq2Sqe Models: Concepts, Trends & Limitations (DLI)
Advanced Natural Language Processing with Apache Spark NLP
Advanced Natural Language Processing with Apache Spark NLP
Recurrent Neural Networks for Recommendations and Personalization with Nick P...
Recurrent Neural Networks for Recommendations and Personalization with Nick P...
Similar to Bhashini (NLTM) Tools
Dhruva - Deploying models at scale.pptx
The document discusses Dhruva, an open-source platform for deploying language AI services at scale. It provides a standardized interface for deploying and collaborating on open AI models. Dhruva aims to optimize models for efficient deployment and provide features like auto-scaling, usage monitoring, and supporting multiple AI tasks. The document demonstrates Dhruva's frontend and describes its technical architecture using technologies like FastAPI, MongoDB, Redis, and Nvidia Triton for optimized model deployment.
Web Annotations – A Game Changer for Language Technology?
Georg Rehm, Felix Sasaki, and Aljoscha Burchardt. Web Annotations - A Game Changer for Language Technologies? I Annotate 2016, Berlin, Germany, May 2016. May 19/20, 2016.
ELKL 5 Language documentation for linguistics and technology
Presentation via Skype at the 5th Endangered and Less Resourced Language Conference, Jharkhand University, India, 2017-02-24.
How AI can help you build better customer relationships?
This document discusses new user experiences with AI and natural language processing. It introduces RASA, an open source conversational AI framework that uses natural language understanding and natural language generation. The document also discusses how RASA works and compares it to alternatives like Google Dialogflow, Microsoft Bot Framework, and Amazon Lex. It suggests that RASA may have limitations for small, domain-specific applications but recent updates allow it to learn intents and word embeddings simultaneously without relying entirely on pre-trained models.
The Standards Mosaic Opening the Way to New Technologies
Presents the mosaic of XML and linked data standards that can support the integration of future natural language technology for the localisation industry
2010 tool forum ata handout
The document provides information about translation tools that will be exhibited at the 2010 American Translators Association conference in Denver. It lists 19 translation tool vendors alphabetically and the tools they offer. It also includes 7 questions asked of each vendor about their tools, such as which tools are for freelance translators versus project managers. Each vendor response is 2 pages and provides details on their tools, new features, and technical information.
AI as a service
Major themes from the Spring 2017 Thoughtworks Technology Radar. Presented to HUstart accelerator on May 17 2017, Hebrew University, Jerusalem.
GDSC career guide presentation.pptx
🚀 *Unlock Your Potential in the Tech World! Explore Your Career Path Today!* 🚀
Are you ready to dive into the exciting realm of technology and shape your career in cutting-edge domains? 🌐📱💻 Whether you're a budding enthusiast or an experienced professional, there's a world of opportunities waiting for you in the fields of Android & Web Development, AI/ML, Cybersecurity, Data Science, PR & Marketing, Designing, Programming Languages and Data Structures.
🔹 *Android & Web Development*: Build the digital future by creating user-friendly apps and responsive websites.
🔹 *AI/ML Enthusiasts*: Join the revolution of Artificial Intelligence and Machine Learning, making computers smarter and more capable of human-like tasks.
🔹 *Cybersecurity Guardians*: Protect digital landscapes from evolving threats, safeguarding sensitive information and ensuring the integrity of systems.
🔹 *Data Science Pioneers*: Dive into data-driven insights, unravel patterns, and make strategic decisions that shape industries and innovations.
🔹 *PR & Marketing Maestros*: Craft compelling narratives, shape brand identities, and influence trends in the fast-paced world of tech communication.
🔹 *Creative Designers*: Fuse technology with artistry; create visually stunning interfaces, logos, and graphics that leave a lasting impact.
🔹 *Coding Champions*: Master programming languages and data structures to engineer solutions that solve real-world challenges.
🔹 *Cloud Computing* Innovators: Harness the power of the cloud, revolutionize accessibility, and drive seamless digital transformation.
Embark on a journey of continuous learning and growth with resources such as online courses, workshops, webinars, and mentorship programs. Your passion, combined with the right knowledge, can lead to a fulfilling career in these dynamic domains. 🌟
Ready to take the next step?
GDSC career guide presentation.pptx
Presentation which was used in the Career Guide session organized by GDSC Guru Nanak Dev University, Regional Campus, Jalandhar hosted on 26 Aug 2023
Improving the User Experience of UiPath Apps
Gain Insight Into Improving the User Experience of UiPath Apps
In this session, you will learn how to build better-looking applications to drive increased adoption
Topics covered :
• Examples of challenging user experience scenarios
• Why does it matter? The impact of a good UX on user adoption
• The 60/30/10 Rule for Color
• The C.R.A.P. Rule for Design
• Short demonstrations and examples of applications that follow these methodologies
Company Overview
MorphoLogic Localisation Company is a Hungarian company established in 2001 that specializes in SAP system translation, consultancy for multilingual projects, and project management. As the only certified partner for SAP Language Services in Hungary, it provides translation, localization, and multilingual support services for SAP implementations. MorphoLogic aims to continuously invest in its knowledge, technology, and employees to best connect organizations across cultures through localization.
Ai/ML services
Ecosmob Technologies is an AI/ML outsourcing company with over 15 years of experience. They have expert programmers and a blend of technical and business skills. They offer end-to-end custom AI/ML services including solutions for image recognition, NLP, chatbots, and more. Their team of over 250 experts uses technologies like TensorFlow, Keras and OpenCV to build state-of-the-art solutions for clients across industries.
Smart cities no ai without ia
This document summarizes a webinar on building smart cities. It discusses using semantic technologies like ontologies, taxonomies, and knowledge graphs to build smart city platforms and applications. Speakers from Semantic Web Company and Findwise discuss semantic data integration, case studies of semantic platforms for healthcare information in Australia and smart city data in Gothenburg, and tools for building semantic solutions like the PoolParty Semantic Suite. The webinar covers challenges in building smart cities and how semantic technologies can help with areas like data modeling, integration, and machine learning on city data. It concludes with a Q&A session.
Gdsc IIIT Surat Orientation 2022.pdf
The document introduces the Google Developer Student Club at IIIT Surat. It discusses their core team, faculty advisor, goals of creating a community of developers and bridging theory and practice. It outlines some of their past events and future plans which include weekly DSA classes, DevHeat, Hacktoberfest, and classes on technologies like Postman and Kotlin. There are also sections on UI/UX design, web and mobile development fundamentals, backend technologies, cloud infrastructure, data analytics, machine learning and how Netflix applies these concepts.
Google Cloud Platform - Cloud-Native Roadshow Stuttgart
This document summarizes a Cloud Native Roadshow presentation in Munich by Marcus Johansson of Google. The presentation covered why cloud infrastructure matters, Google's global infrastructure including data centers and networking, and Google Cloud Platform products and services like Compute Engine, Kubernetes Engine, Cloud Spanner, Cloud ML, and AI/ML APIs for vision, speech, translation, and more. It also discussed advantages of running Cloud Foundry on Google Cloud Platform.
NLP based Data Engineering and ETL Tool - Ask On Data.pdf
Ask On Data is World's first chat & AI based data pipeline tool which can be used by anyone. It can act as your AI assistance for all of your data related requirements.
The biggest USPs of Ask On Data are
- No learning curve: With a simple chat based interface all you need is to type and your entire data transformations can happen.
- NO technical knowledge required. Even non tech person can use
- Chat interface : Allowing anyone to use
- Data transformations at the speed of typing. You can save approx 93% of time in doing any data engineering work.
- Save money by decouping processing
Ask On Data can be used for various tasks like Data Loading/Migration from one source to target, Data Warehouse population, Data Lake population, Data Wrangling and Data Cleaning, Data Integration. This tool can be used by AI and ML to get clean processed data for their AI ML algorithms. This an be used by Data Engineers to create data pipelines for Data Warehoue, Data lake or Reporting DB population. It can also be used by Data Analyst/ Business Analyst/BI developers to get calculated processed data for doing further data visualization and analysis for senior management and busiess leaders.
Get in touch on support@askondata.com
Conversational Artificial Intelligence with Ben Tomlinson and Wayne Thompson
Communicating information to each other is at the heart of the human experience. Data, and the analysis of it, often drives this communication in a business setting. This session aims to give you an understanding of how advances in Artificial Intelligence, specifically Natural Language Interaction (NLI), Natural Language Generation coupled with Deep Learning can create new and exciting opportunities for building analytical based chatbots.
We will talk about how to design and train an NLI system that map requests to deep learning pipelines to derive insights. You will also learn how to apply NLG templates to help facilitate improved understanding and interaction with the chat-bot.
Translation as a professional activity
The document discusses translation as a professional activity and outlines the necessary skills, tools, and market considerations. It covers the skills required of translators, including linguistic competence, technical skills, research abilities, and more. It also discusses common translation tools like CAT tools, translation memory systems, and terminology databases. Regarding the market, it provides statistics on customer profiles, top client sectors and languages, and labor market information for translators. It emphasizes the importance of continuous learning, developing new skills, and staying up-to-date with the latest tools for professional success in translation.
Sudipta_Mukherjee_Resume-Nov_2022.pdf
Sudipta Mukherjee is a software engineer, author, and speaker with over 18 years of experience. He has written 6 books on programming topics such as machine learning using F# and source code analytics. He is currently a freelance software developer working on projects involving source code analysis, process mining, and financial transaction modeling using languages like C#, F#, and Python. Previously he held positions as a lead developer and technical lead at various companies where he developed tools for code analysis, data analytics, and domain-specific languages. He is recognized as an expert in F# and his interests include machine learning, algorithms, and functional programming.
Google Cloud Platform Munich
This document summarizes a presentation about Google Cloud Platform capabilities. It discusses Google's global infrastructure including data centers, networking capabilities, and services. It provides overviews of key services like Compute Engine, Kubernetes Engine, Cloud Spanner, Cloud ML, various machine learning APIs, and how Cloud Foundry can leverage GCP infrastructure and services. The presentation aims to demonstrate how GCP enables cloud-native applications and no-touch operations at global scale.
Similar to Bhashini (NLTM) Tools (20)
Web Annotations – A Game Changer for Language Technology?
Web Annotations – A Game Changer for Language Technology?
ELKL 5 Language documentation for linguistics and technology
ELKL 5 Language documentation for linguistics and technology
How AI can help you build better customer relationships?
How AI can help you build better customer relationships?
The Standards Mosaic Opening the Way to New Technologies
The Standards Mosaic Opening the Way to New Technologies
Google Cloud Platform - Cloud-Native Roadshow Stuttgart
Google Cloud Platform - Cloud-Native Roadshow Stuttgart
NLP based Data Engineering and ETL Tool - Ask On Data.pdf
NLP based Data Engineering and ETL Tool - Ask On Data.pdf
Conversational Artificial Intelligence with Ben Tomlinson and Wayne Thompson
Conversational Artificial Intelligence with Ben Tomlinson and Wayne Thompson
Recently uploaded
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Building RAG with self-deployed Milvus vector database and Snowpark Container...
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Recently uploaded (20)
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Building RAG with self-deployed Milvus vector database and Snowpark Container...
Building RAG with self-deployed Milvus vector database and Snowpark Container...
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Bhashini (NLTM) Tools
- 1. Bhashini Tools BhashaDaan & ULCA
- 2. Agenda ● Bhashini Mission ● Need for Digital Infrastructure ● NLTM Architecture ● Datasets & Models ● BhashaDaan ● ULCA ● Contributing Datasets & Models to ULCA ● Roadmap
- 3. Bhashini Mission Statement Create a knowledge-based society by transcending the language barriers ; Providing content and services to citizens, in their own language.
- 4. Digital Infrastructure Educational eBooks Digital Web Content Tele Services Communication Services Knowledge Base Search Datasets Datasets AI/ML Models
- 6. In simple words… Contributors of Datasets Development of AI Models Development of End user Applications
- 7. Datasets & Contributors Contributors Datasets Public - Crowdsourced - Free Dedicated Teams - Language Experts - Specific Tasks - Paid Parallel Monolingual ASR TTS OCR & more…
- 8. Data Collection Help to build an open repository of data to digitally enrich your language ASR Datasets TTS Datasets Parallel Datasets OCR Datasets
- 9. BashaDhaan - A short video
- 10. AI Models Task Types Contributors Translation ASR TTS Transliteration OCR Models EkStep AI4Bharat IITs IIITs CDAC IndicTrans Vakyansh IndicXlit IndicTTS Anuvaad and more… and more… and more…
- 11. ULCA stands for Universal Language Contribution APIs ULCA ULCA is a standard API and open scalable data platform (supporting various types of datasets) for Indian language datasets and models. World’s largest Indic language data and models platform for Open AI innovation
- 12. ULCA - Components Open and scalable data platform ● Parallel text corpus in two or more languages ● Monolingual text corpus ● Automatic Speech Recognition (ASR) corpus ● Text to Speech (TTS) corpus ● Optical Character Recognition (OCR) corpus ● Natural Language Understanding (NLU) datasets ● Machine Translation (MT) ● Automatic Speech Recognition (ASR) ● Text to Speech (TTS) ● Optical Character Recognition (OCR) ● Transliteration ● Large, diverse and task specific benchmarks ● Research community approved metric system Inclusive Indian language Models Automated Transparent Benchmarking
- 13. ULCA - Current Status Datasets ● 215 Million Parallel sentences in 13 languages ● 14k Hours of Audio recording in 14 languages ● 2.5 Million Images for OCR in 12 languages ● 10 Million Transliteration pairs in 19 languages World's largest Indic language data and models platform for open AI innovation Models ● 240 State of the Art Models in 21 Indian languages across Translation, speech (ASR/TTS), OCR & Transliteration Benchmarks ● 135 Open Benchmarks across Translation, ASR & Transliteration in 20 Indian languages
- 14. ULCA- Actions Datasets Submission My Contribution Search & Download My Searches Models Benchmarking Submission My Contribution Explore Models Try Model Metrics Benchmark Dataset Explore Models Try Model Model Feedback Model Leaderboard
- 15. Contributing Datasets to ULCA
- 16. Contributing Models to ULCA
- 17. ULCA - Language AI Models Demo
- 18. ULCA - Roadmap Datasets POS, NER Multi-lingual Multi-speaker Mobile APK Models POS, NER Benchmark OCR Benchmark dataset User Analytics Ex : En-Hi Legal Readymade Datasets Realtime Inference for Models
- 19. ULCA - Roadmap (Contd.) ULCA Automated Ingestion of verified contents from external sources to ULCA
Editor's Notes
- <a href="https://www.freepik.com/vectors/robot-head">Robot head vector created by pch.vector - www.freepik.com</a>