LLM Architectures - A Comprehensive Guide: BERT, BART, XLNET
LLM Architectures - A Comprehensive Guide: BERT, BART, XLNET

Author: Anand Vemula

Publisher: Anand Vemula

Published:

Total Pages: 36

ISBN-13:

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Demystifying the Power of Large Language Models: A Guide for Everyone Large Language Models (LLMs) are revolutionizing the way we interact with machines and information. This comprehensive guide unveils the fascinating world of LLMs, guiding you from their fundamental concepts to their cutting-edge applications. Master the Basics: Explore the foundational architectures like Recurrent Neural Networks (RNNs) and Transformers that power LLMs. Gain a clear understanding of how these models process and understand language. Deep Dives into Pioneering Architectures: Delve into the specifics of BERT, BART, and XLNet, three groundbreaking LLM architectures. Learn about their unique pre-training techniques and how they tackle various natural language processing tasks. Unveiling the Champions: A Comparative Analysis: Discover how these leading LLM architectures stack up against each other. Explore performance benchmarks and uncover the strengths and weaknesses of each model to understand which one is best suited for your specific needs. Emerging Frontiers: Charting the Course for the Future: Explore the exciting trends shaping the future of LLMs. Learn about the quest for ever-larger models, the growing focus on training efficiency, and the development of specialized architectures for tasks like question answering and dialogue systems. This book is not just about technical details. It provides real-world case studies and use cases, showcasing how LLMs are transforming various industries, from content creation and customer service to healthcare and education. With clear explanations and a conversational tone, this guide is perfect for anyone who wants to understand the power of LLMs and their potential impact on our world. Whether you're a tech enthusiast, a student, or a professional curious about the future of AI, this book is your one-stop guide to demystifying Large Language Models.

The Reading Mind
The Reading Mind

Author: Daniel T. Willingham

Publisher: John Wiley & Sons

Published: 2017-04-10

Total Pages: 203

ISBN-13: 111930136X

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A Map to the Magic of Reading Stop for a moment and wonder: what's happening in your brain right now—as you read this paragraph? How much do you know about the innumerable and amazing connections that your mind is making as you, in a flash, make sense of this request? Why does it matter? The Reading Mind is a brilliant, beautifully crafted, and accessible exploration of arguably life's most important skill: reading. Daniel T. Willingham, the bestselling author of Why Don't Students Like School?, offers a perspective that is rooted in contemporary cognitive research. He deftly describes the incredibly complex and nearly instantaneous series of events that occur from the moment a child sees a single letter to the time they finish reading. The Reading Mind explains the fascinating journey from seeing letters, then words, sentences, and so on, with the author highlighting each step along the way. This resource covers every aspect of reading, starting with two fundamental processes: reading by sight and reading by sound. It also addresses reading comprehension at all levels, from reading for understanding at early levels to inferring deeper meaning from texts and novels in high school. The author also considers the undeniable connection between reading and writing, as well as the important role of motivation as it relates to reading. Finally, as a cutting-edge researcher, Willingham tackles the intersection of our rapidly changing technology and its effects on learning to read and reading. Every teacher, reading specialist, literacy coach, and school administrator will find this book invaluable. Understanding the fascinating science behind the magic of reading is essential for every educator. Indeed, every "reader" will be captivated by the dynamic but invisible workings of their own minds.

Biomedical Natural Language Processing
Biomedical Natural Language Processing

Author: Kevin Bretonnel Cohen

Publisher: John Benjamins Publishing Company

Published: 2014-02-15

Total Pages: 174

ISBN-13: 9027271062

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Biomedical Natural Language Processing is a comprehensive tour through the classic and current work in the field. It discusses all subjects from both a rule-based and a machine learning approach, and also describes each subject from the perspective of both biological science and clinical medicine. The intended audience is readers who already have a background in natural language processing, but a clear introduction makes it accessible to readers from the fields of bioinformatics and computational biology, as well. The book is suitable as a reference, as well as a text for advanced courses in biomedical natural language processing and text mining.

Natural Language Processing with Transformers, Revised Edition
Natural Language Processing with Transformers, Revised Edition

Author: Lewis Tunstall

Publisher: "O'Reilly Media, Inc."

Published: 2022-05-26

Total Pages: 409

ISBN-13: 1098136764

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Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments

Speech-to-Speech Translation
Speech-to-Speech Translation

Author: Yutaka Kidawara

Publisher: Springer Nature

Published: 2019-11-22

Total Pages: 103

ISBN-13: 9811505950

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This book provides the readers with retrospective and prospective views with detailed explanations of component technologies, speech recognition, language translation and speech synthesis. Speech-to-speech translation system (S2S) enables to break language barriers, i.e., communicate each other between any pair of person on the glove, which is one of extreme dreams of humankind. People, society, and economy connected by S2S will demonstrate explosive growth without exception. In 1986, Japan initiated basic research of S2S, then the idea spread world-wide and were explored deeply by researchers during three decades. Now, we see S2S application on smartphone/tablet around the world. Computational resources such as processors, memories, wireless communication accelerate this computation-intensive systems and accumulation of digital data of speech and language encourage recent approaches based on machine learning. Through field experiments after long research in laboratories, S2S systems are being well-developed and now ready to utilized in daily life. Unique chapter of this book is end-2-end evaluation by comparing system’s performance and human competence. The effectiveness of the system would be understood by the score of this evaluation. The book will end with one of the next focus of S2S will be technology of simultaneous interpretation for lecture, broadcast news and so on.

Data Science on AWS
Data Science on AWS

Author: Chris Fregly

Publisher: "O'Reilly Media, Inc."

Published: 2021-04-07

Total Pages: 524

ISBN-13: 1492079367

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With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

API Standard for MCUs
API Standard for MCUs

Author: Jacob Beningo

Publisher:

Published: 2017-08-04

Total Pages: 374

ISBN-13: 9781973972204

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Embedded software has traditionally been developed as a one-off software development effort designed for an individual product. In recent years, embedded system complexity has dramatically increased and the microcontrollers capabilities have followed. What were once simple 8-bit computing machines running at a few dozen megahertz have now become full-fledged 32-bit processors executing at hundreds of megahertz's. Developing software from scratch or for use in a single application or processor has become extremely costly and problematic for design teams. This API standard for microcontrollers is an example hardware abstraction layer designed to help embedded software developers designing products with microcontrollers create reusable software that abstracts out the hardware. This API standard has been developed and used in production systems for more than half a decade in devices ranging from automotive and medical devices to space systems. Each iteration that it has gone through has helped create a standard that flexible for developers and meets many general real-time design needs. Using an API to abstract out the microcontroller has several major benefits to development teams such as: Removing the specialized need to master the microcontroller hardware Decreasing costs through reusable firmware Faster times to market Better planning and accuracy in the development cycle Portability and flexibility to handle numerous applications Undoubtedly there are many more benefits but in this book the goal is to provide the reader with the standard. If you are interested in understanding how to develop reusable software and the processes that a developer would go through to create their own API's and HAL's, the companion book "Developing Reusable Firmware : A Practical Approach", can be found at www.beningo.com. Developing Reusable Firmware discusses the key ideas behind creating API's and HAL's along with the processes and design considerations that developers need to consider when creating their own. This standard example has gone through many iterations and has become very stable but there is always an opportunity that changes will be made in the future. In order to stay up to date and receive the latest information and also receive the associated API template files, please visit the associated webpage at https://www.beningo.com/api/index.php to sign-up. When you sign-up you will receive Doxygen template source files that layout the entire standard in way that can be easily modified to implement in your own development cycle. I wish you the best of luck in using this standard and dramatically transforming the way in which you develop and reuse your embedded software.

Machine Learning: ECML 2004
Machine Learning: ECML 2004

Author: Jean-Francois Boulicaut

Publisher: Springer

Published: 2004-11-05

Total Pages: 597

ISBN-13: 3540301151

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The proceedings of ECML/PKDD 2004 are published in two separate, albeit - tertwined,volumes:theProceedingsofthe 15thEuropeanConferenceonMac- ne Learning (LNAI 3201) and the Proceedings of the 8th European Conferences on Principles and Practice of Knowledge Discovery in Databases (LNAI 3202). The two conferences were co-located in Pisa, Tuscany, Italy during September 20–24, 2004. It was the fourth time in a row that ECML and PKDD were co-located. - ter the successful co-locations in Freiburg (2001), Helsinki (2002), and Cavtat- Dubrovnik (2003), it became clear that researchersstrongly supported the or- nization of a major scienti?c event about machine learning and data mining in Europe. We are happy to provide some statistics about the conferences. 581 di?erent papers were submitted to ECML/PKDD (about a 75% increase over 2003); 280 weresubmittedtoECML2004only,194weresubmittedtoPKDD2004only,and 107weresubmitted to both.Aroundhalfofthe authorsforsubmitted papersare from outside Europe, which is a clear indicator of the increasing attractiveness of ECML/PKDD. The Program Committee members were deeply involved in what turned out to be a highly competitive selection process. We assigned each paper to 3 - viewers, deciding on the appropriate PC for papers submitted to both ECML and PKDD. As a result, ECML PC members reviewed 312 papers and PKDD PC members reviewed 269 papers. We accepted for publication regular papers (45 for ECML 2004 and 39 for PKDD 2004) and short papers that were as- ciated with poster presentations (6 for ECML 2004 and 9 for PKDD 2004). The globalacceptance ratewas14.5%for regular papers(17% if we include the short papers).

Representation Learning for Natural Language Processing
Representation Learning for Natural Language Processing

Author: Zhiyuan Liu

Publisher: Springer Nature

Published: 2020-07-03

Total Pages: 319

ISBN-13: 9811555737

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This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

Applied Artificial Intelligence
Applied Artificial Intelligence

Author: Mariya Yao

Publisher:

Published: 2018-04-30

Total Pages: 246

ISBN-13: 9780998289021

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This bestselling book gives business leaders and executives a foundational education on how to leverage artificial intelligence and machine learning solutions to deliver ROI for your business.