Transfer Learning And Robustness For Natural Language Processing
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Transfer Learning And Robustness For Natural Language Processing
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Author : Di Jin (Ph.D.)
language : en
Publisher:
Release Date : 2020
Transfer Learning And Robustness For Natural Language Processing written by Di Jin (Ph.D.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Teaching machines to understand human language is one of the most elusive and long-standing challenges in Natural Language Processing (NLP). Driven by the fast development of deep learning, state-of-the-art NLP models have already achieved human-level performance in various large benchmark datasets, such as SQuAD, SNLI, and RACE. However, when these strong models are deployed to real-world applications, they often show poor generalization capability in two situations: 1. There is only a limited amount of data available for model training; 2. Deployed models may degrade significantly in performance on noisy test data or natural/artificial adversaries. In short, performance degradation on low-resource tasks/datasets and unseen data with distribution shifts imposes great challenges to the reliability of NLP models and prevent them from being massively applied in the wild. This dissertation aims to address these two issues. Towards the first one, we resort to transfer learning to leverage knowledge acquired from related data in order to improve performance on a target low-resource task/dataset. Specifically, we propose different transfer learning methods for three natural language understanding tasks: multi-choice question answering, dialogue state tracking, and sequence labeling, and one natural language generation task: machine translation. These methods are based on four basic transfer learning modalities: multi-task learning, sequential transfer learning, domain adaptation, and cross-lingual transfer. We show experimental results to validate that transferring knowledge from related domains, tasks, and languages can improve the target task/dataset significantly. For the second issue, we propose methods to evaluate the robustness of NLP models on text classification and entailment tasks. On one hand, we reveal that although these models can achieve a high accuracy of over 90%, they still easily crash over paraphrases of original samples by changing only around 10% words to their synonyms. On the other hand, by creating a new challenge set using four adversarial strategies, we find even the best models for the aspect-based sentiment analysis task cannot reliably identify the target aspect and recognize its sentiment accordingly. On the contrary, they are easily confused by distractor aspects. Overall, these findings raise great concerns of robustness of NLP models, which should be enhanced to ensure their long-run stable service.
Transfer Learning For Natural Language Processing
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Author : Paul Azunre
language : en
Publisher: Simon and Schuster
Release Date : 2021-08-31
Transfer Learning For Natural Language Processing written by Paul Azunre and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Computers categories.
Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions
Ultimate Llama For Natural Language Processing Nlp Build Fine Tune And Scale Next Generation Nlp Solutions With Llama To Power Future Ready Ai Systems
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Author : Gaurav Singh
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-09-29
Ultimate Llama For Natural Language Processing Nlp Build Fine Tune And Scale Next Generation Nlp Solutions With Llama To Power Future Ready Ai Systems written by Gaurav Singh and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-29 with Computers categories.
Build, Scale and Optimize Cutting-Edge NLP with Llama for Next Gen AI. Key Features● Explore Llama’s evolution and innovations for next-gen NLP.● Implement real-world NLP tasks with step-by-step examples.● Fine-tune, optimize, and deploy Llama at enterprise scale. Book DescriptionLlama models have rapidly emerged as a cornerstone in natural language processing, redefining how AI systems understand and generate human language. From their efficient architecture to the cutting-edge advancements in Llama 4, these models enable enterprises, researchers, and developers to build powerful, scalable, and responsible NLP solutions. This book, Ultimate Llama for Natural Language Processing (NLP), takes you on a structured journey through the evolution and applications of Llama. It begins with the foundations of the Llama series and its architecture, before progressing to core NLP tasks such as classification, summarization, sentiment analysis, and conversational AI. Subsequent chapters cover fine-tuning, transfer learning, optimization, and deployment at enterprise scale, with practical insights into real-world industry use cases. The book also addresses troubleshooting, ethical AI, and the future of multimodal and sparse Mixture-of-Experts models. Thus, by the end, readers will be well-equipped to train, adapt, and deploy Llama models across domains such as healthcare, finance, and customer engagement. What you will learn● Understand Llama’s evolution, architecture, and unique innovations in NLP.● Implement core NLP tasks like classification, NER, and summarization.● Fine-tune Llama for custom domains using advanced transfer learning.● Optimize inference speed, and deploy Llama models at enterprise scale.● Troubleshoot, monitor, and continuously improve Llama model performance.● Apply Llama 4 to real-world industry use cases and multimodal AI.
Applied Spacy Practical Techniques For Building Robust Nlp Pipelines And Production Systems
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Author : William E Clark
language : en
Publisher: Walzone Press
Release Date : 2025-10-30
Applied Spacy Practical Techniques For Building Robust Nlp Pipelines And Production Systems written by William E Clark and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-30 with Computers categories.
Applied spaCy: Practical Techniques for Building Robust NLP Pipelines and Production Systems is a hands‑on guide that teaches engineers and researchers how to design, build, and maintain industrial-grade NLP solutions using spaCy. Grounded in real-world workflows, the book moves beyond tutorials to demonstrate practical decisions and patterns for reliable, scalable systems—covering environment and dependency management, cross-platform deployment, and how spaCy fits within the broader NLP ecosystem alongside tools like NLTK, CoreNLP, Stanza, and Transformers. The core chapters unpack spaCy’s data structures and pipeline mechanics—tokenization, segmentation, morphological analysis, part‑of‑speech tagging, dependency parsing, and named entity recognition—while emphasizing extensibility and error analysis. Readers learn to craft custom components, mix rule‑based and statistical approaches, handle multilingual and large‑scale data, and profile and optimize pipelines for throughput and latency. Annotation best practices, tooling for quality control, and techniques for integrating spaCy with scikit‑learn and transformer models are presented as pragmatic, repeatable patterns. Advanced sections cover the full model lifecycle: preparing data, training and fine‑tuning models, active learning, explainability, privacy considerations, and deploying models to cloud and on‑device environments. The book closes with production engineering guidance—monitoring, versioning, testing, and continuous improvement—alongside a call to contribute to the spaCy ecosystem and to adopt responsible, ethical practices when delivering NLP systems in the real world.
Artificial Neural Networks And Machine Learning Icann 2021
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Author : Igor Farkaš
language : en
Publisher: Springer Nature
Release Date : 2021-09-11
Artificial Neural Networks And Machine Learning Icann 2021 written by Igor Farkaš and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-11 with Computers categories.
The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as adversarial machine learning, anomaly detection, attention and transformers, audio and multimodal applications, bioinformatics and biosignal analysis, capsule networks and cognitive models. *The conference was held online 2021 due to the COVID-19 pandemic.
Deep Learning And Ai Superhero
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Author : Cuantum Technologies LLC
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-20
Deep Learning And Ai Superhero written by Cuantum Technologies LLC and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-20 with Computers categories.
Master TensorFlow, Keras, and PyTorch for deep learning in AI applications. Learn neural networks, CNNs, RNNs, LSTMs, and GANs through hands-on exercises and real-world projects. Key Features TensorFlow, Keras, and PyTorch for diverse deep learning frameworks Neural network concepts with real-world industry relevance Cloud and edge AI deployment techniques for scalable solutions Book DescriptionDive into the world of deep learning with this comprehensive guide that bridges theory and practice. From foundational neural networks to advanced architectures like CNNs, RNNs, and Transformers, this book equips you with the tools to build, train, and optimize AI models using TensorFlow, Keras, and PyTorch. Clear explanations of key concepts such as gradient descent, loss functions, and backpropagation are combined with hands-on exercises to ensure practical understanding. Explore cutting-edge AI frameworks, including generative adversarial networks (GANs) and autoencoders, while mastering real-world applications like image classification, text generation, and natural language processing. Detailed chapters cover transfer learning, fine-tuning pretrained models, and deployment strategies for cloud and edge computing. Practical exercises and projects further solidify your skills as you implement AI solutions for diverse challenges. Whether you're deploying AI models on cloud platforms like AWS or optimizing them for edge devices with TensorFlow Lite, this book provides step-by-step guidance. Designed for developers, AI enthusiasts, and data scientists, it balances theoretical depth with actionable insights, making it the ultimate resource for mastering modern deep learning frameworks and advancing your career in AIWhat you will learn Understand neural network basics Build models using TensorFlow and Keras Train and optimize PyTorch models Apply CNNs for image recognition Use RNNs and LSTMs for sequence tasks Leverage Transformers in NLP Who this book is for This book is for software developers, AI enthusiasts, data scientists, and ML engineers who aim to master deep learning frameworks. A foundational understanding of programming and basic ML concepts is recommended. Ideal for those seeking hands-on experience in real-world AI projects.
Transforming The Healthcare Revenue Cycle With Artificial Intelligence
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Author : Korin Reid
language : en
Publisher: CRC Press
Release Date : 2025-08-08
Transforming The Healthcare Revenue Cycle With Artificial Intelligence written by Korin Reid and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-08 with Business & Economics categories.
Revenue cycle management (RCM) refers to an institution’s financial management process that helps track, identify, collect, and manage incoming payments. This process helps businesses foster financial transparency within the company and charge patients the correct amount for the services they receive. But because of the unique healthcare payment system in the United States, relatively few of these dollars change hands directly between providers and their patients. Instead, there is a complex reimbursement system, mostly driven by third-party payment transactions between government programs and insurance companies, on the one hand, and healthcare providers, on the other. Artificial intelligence (AI) can help predict claim denials by analyzing past denial trends and alerting health information management (HIM) professionals of potential denials in advance of billing. This affords an opportunity to review and correct claims pre-bill. One major benefit of AI in RCM is increased efficiency. By automating routine tasks, healthcare organizations can free up staff to focus on more important and value-added work. This can lead to improved productivity and faster turnaround times, ultimately resulting in improved patient care. This book provides an informative blueprint to help hospital and healthcare revenue cycle administration personnel along their AI journey by using the most commonly available administrative datasets, electronic claims, and electronic health records. Peppered throughout the book are hilarious anecdotes and cautionary tales from the author’s experience in building AI solutions in the healthcare space. The book begins with an overview of key concepts such as data science, machine learning, AI, language models (e.g., ChatGPT), and more. The author expands on the defined process in the context of common revenue cycle use cases that leverage electronic claims and electronic health records. Finally, the book provides guidance on how to evaluate AI solutions at each point of the development process, including third-party vendor AI solutions.
Proceedings Of The 1986 Ieee International Conference On Systems Man And Cybernetics
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Author :
language : en
Publisher:
Release Date : 1986
Proceedings Of The 1986 Ieee International Conference On Systems Man And Cybernetics written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with Cybernetics categories.
The Tenth Conference On Artificial Intelligence For Applications
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Author :
language : en
Publisher:
Release Date : 1994
The Tenth Conference On Artificial Intelligence For Applications written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Artificial intelligence categories.
Proceedings Of Coling
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Author :
language : en
Publisher:
Release Date : 1996
Proceedings Of Coling written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Applied linguistics categories.