Explainable Natural Language Processing
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Explainable Natural Language Processing
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Author : Anders Søgaard
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Explainable Natural Language Processing written by Anders Søgaard and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
This book presents a taxonomy framework and survey of methods relevant to explaining the decisions and analyzing the inner workings of Natural Language Processing (NLP) models. The book is intended to provide a snapshot of Explainable NLP, though the field continues to rapidly grow. The book is intended to be both readable by first-year M.Sc. students and interesting to an expert audience. The book opens by motivating a focus on providing a consistent taxonomy, pointing out inconsistencies and redundancies in previous taxonomies. It goes on to present (i) a taxonomy or framework for thinking about how approaches to explainable NLP relate to one another; (ii) brief surveys of each of the classes in the taxonomy, with a focus on methods that are relevant for NLP; and (iii) a discussion of the inherent limitations of some classes of methods, as well as how to best evaluate them. Finally, the book closes by providing a list of resources for further research on explainability.
Explainable Ai With Python
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Author : Leonida Gianfagna
language : en
Publisher: Springer Nature
Release Date : 2021-04-28
Explainable Ai With Python written by Leonida Gianfagna and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-28 with Computers categories.
This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches presented can be applied to almost all the current “machine learning” models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others. Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI. Beginning with examples of what Explainable AI (XAI) is and why it is needed in the field, the book details different approaches to XAI depending on specific context and need. Hands-on work on interpretable models with specific examples leveraging Python are then presented, showing how intrinsic interpretable models can be interpreted and how to produce “human understandable” explanations. Model-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are “opaque.” Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for the future. Taking a practical perspective, the authors demonstrate how to effectively use ML and XAI in science. The final chapter explains Adversarial Machine Learning and how to do XAI with adversarial examples.
Practical Explainable Ai Using Python
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Author : Pradeepta Mishra
language : en
Publisher: Apress
Release Date : 2021-12-15
Practical Explainable Ai Using Python written by Pradeepta Mishra and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-15 with Computers categories.
Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data, classification problems, and natural language processing–related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks. What You'll Learn Review the different ways of making an AI model interpretable and explainable Examine the biasness and good ethical practices of AI models Quantify, visualize, and estimate reliability of AI models Design frameworks to unbox the black-box models Assess the fairness of AI models Understand the building blocks of trust in AI models Increase the level of AI adoption Who This Book Is For AI engineers, data scientists, and software developers involved in driving AI projects/ AI products.
Deep Learning In Gaming And Animations
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Author : Vikas Chaudhary
language : en
Publisher: CRC Press
Release Date : 2021-12-07
Deep Learning In Gaming And Animations written by Vikas Chaudhary and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-07 with Computers categories.
Over the last decade, progress in deep learning has had a profound and transformational effect on many complex problems, including speech recognition, machine translation, natural language understanding, and computer vision. As a result, computers can now achieve human-competitive performance in a wide range of perception and recognition tasks. Many of these systems are now available to the programmer via a range of so-called cognitive services. More recently, deep reinforcement learning has achieved ground-breaking success in several complex challenges. This book makes an enormous contribution to this beautiful, vibrant area of study: an area that is developing rapidly both in breadth and depth. Deep learning can cope with a broader range of tasks (and perform those tasks to increasing levels of excellence). This book lays a good foundation for the core concepts and principles of deep learning in gaming and animation, walking you through the fundamental ideas with expert ease. This book progresses in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into its applications. Also, some chapters introduce and cover novel ideas about how artificial intelligence (AI), deep learning, and machine learning have changed the world in gaming and animation. It gives us the idea that AI can also be applied in gaming, and there are limited textbooks in this area. This book comprehensively addresses all the aspects of AI and deep learning in gaming. Also, each chapter follows a similar structure so that students, teachers, and industry experts can orientate themselves within the text. There are few books in the field of gaming using AI. Deep Learning in Gaming and Animations teaches you how to apply the power of deep learning to build complex reasoning tasks. After being exposed to the foundations of machine and deep learning, you will use Python to build a bot and then teach it the game's rules. This book also focuses on how different technologies have revolutionized gaming and animation with various illustrations.
Explainable Natural Language Processing
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Author : Anders Søgaard
language : en
Publisher:
Release Date : 2021-09-22
Explainable Natural Language Processing written by Anders Søgaard and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-22 with categories.
This book presents a taxonomy framework and survey of methods relevant to explaining the decisions and analyzing the inner workings of Natural Language Processing (NLP) models. The book is intended to provide a snapshot of Explainable NLP, though the field continues to rapidly grow. The book is intended to be both readable by first-year M.Sc. students and interesting to an expert audience. The book opens by motivating a focus on providing a consistent taxonomy, pointing out inconsistencies and redundancies in previous taxonomies. It goes on to present (i) a taxonomy or framework for thinking about how approaches to explainable NLP relate to one another; (ii) brief surveys of each of the classes in the taxonomy, with a focus on methods that are relevant for NLP; and (iii) a discussion of the inherent limitations of some classes of methods, as well as how to best evaluate them. Finally, the book closes by providing a list of resources for further research on explainability.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Practical Natural Language Processing
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Author : Sowmya Vajjala
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-17
Practical Natural Language Processing written by Sowmya Vajjala and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-17 with Computers categories.
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective
Explainable Artificial Intelligence In The Healthcare Industry
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Author : Abhishek Kumar
language : en
Publisher: John Wiley & Sons
Release Date : 2025-04-08
Explainable Artificial Intelligence In The Healthcare Industry written by Abhishek Kumar and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-08 with Computers categories.
Discover the essential insights and practical applications of explainable AI in healthcare that will empower professionals and enhance patient trust with Explainable AI in the Healthcare Industry, a must-have resource. Explainable AI (XAI) has significant implications for the healthcare industry, where trust, accountability, and interpretability are crucial factors for the adoption of artificial intelligence. XAI techniques in healthcare aim to provide clear and understandable explanations for AI-driven decisions, helping healthcare professionals, patients, and regulatory bodies to better comprehend and trust the AI models’ outputs. Explainable AI in the Healthcare Industry presents a comprehensive exploration of the critical role of explainable AI in revolutionizing the healthcare industry. With the rapid integration of AI-driven solutions in medical practice, understanding how these models arrive at their decisions is of paramount importance. The book delves into the principles, methodologies, and practical applications of XAI techniques specifically tailored for healthcare settings.
Natural Language Processing With Nltk
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-13
Natural Language Processing With Nltk written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-13 with Computers categories.
"Natural Language Processing with NLTK" "Natural Language Processing with NLTK" is an essential resource for professionals and researchers seeking an in-depth, practical guide to modern language technologies in Python. The book navigates the full spectrum of NLP, beginning with the foundations of the field, the evolution of computational linguistics, and the architectural design of NLTK, the leading open-source Python library for natural language processing. Readers are guided through sophisticated environment setup, optimization techniques, and an advanced understanding of NLTK’s extensible framework, ensuring a robust footing to tackle even the most complex text workloads. Each chapter delves into the essential components of real-world NLP pipelines: from nuanced tokenization and adaptable preprocessing strategies to sophisticated morphological analysis, multilingual part-of-speech tagging, and custom text normalization workflows. The volume offers comprehensive insights into syntactic parsing, grammatical engineering, semantic analysis—inclusive of advanced WordNet queries, word sense disambiguation, contextual embeddings, and semantic role labeling—culminating with practical methodologies for information extraction, named entity recognition, and coreference resolution. The integration of machine learning is thoroughly explored, bridging classical models with contemporary deep learning frameworks, and equipping practitioners with proven strategies for feature engineering, classification, topic modeling, and ensemble learning. Moving beyond implementation, the book addresses the non-technical dimensions of NLP, such as deployment at scale, API and microservices design, effective monitoring, and model lifecycle management. The final chapters cast a vision for future research, emphasizing ethical AI, explainability, fairness, and the unique challenges posed by low-resource and multilingual settings. With its blend of theoretical rigor and production-focused practicality, "Natural Language Processing with NLTK" stands as an authoritative guide for developing robust, ethical, and scalable NLP solutions in today’s fast-evolving landscape.
Spacy For Natural Language Processing
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-05-29
Spacy For Natural Language Processing written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-29 with Computers categories.
"SpaCy for Natural Language Processing" "SpaCy for Natural Language Processing" is a comprehensive guide to mastering one of the industry’s leading natural language processing (NLP) frameworks. Through a carefully structured progression, the book introduces SpaCy’s core philosophies and modern architecture, offering a foundation for readers who seek both theoretical knowledge and practical expertise. The opening chapters dissect SpaCy’s essential data structures, advanced pipeline mechanics, and provide clear guidance on installation, environment management, and compatibility across platforms. The text also situates SpaCy within the broader NLP landscape with thorough comparisons to frameworks like NLTK, CoreNLP, Stanza, and Transformers. The book delves deeply into the mechanics of tokenization, segmentation, and linguistic annotation, equipping practitioners to handle challenging multilingual and large-scale data scenarios. Readers will explore state-of-the-art workflows for part-of-speech tagging, morphological analysis, dependency parsing, and named entity recognition, with an emphasis on extensibility, error analysis, and annotation best practices. A significant focus is given to building and customizing NLP pipelines, covering topics such as crafting custom components, integrating statistical and rule-based logic, profiling for performance, and deploying robust pipelines at scale. Advanced chapters address the full lifecycle of model development: from data preparation and model training to fine-tuning, deployment, and integration with machine learning libraries like scikit-learn and Transformers. Cutting-edge topics—active learning, explainability, privacy, on-device deployment, and benchmarking—are explored alongside guidance for maintaining production-grade workflows. The concluding chapters encourage readers to adopt best practices, contribute to the evolving SpaCy ecosystem, and reflect critically on ethical and responsible AI in NLP, making this book a vital resource for engineers, researchers, and forward-thinking NLP practitioners.