Applied Machine Learning Explainability Techniques
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Applied Machine Learning Explainability Techniques
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Author : Aditya Bhattacharya
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-07-29
Applied Machine Learning Explainability Techniques written by Aditya Bhattacharya 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 2022-07-29 with Computers categories.
Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems Key Features • Explore various explainability methods for designing robust and scalable explainable ML systems • Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems • Design user-centric explainable ML systems using guidelines provided for industrial applications Book Description Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases. Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users. By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered. What you will learn • Explore various explanation methods and their evaluation criteria • Learn model explanation methods for structured and unstructured data • Apply data-centric XAI for practical problem-solving • Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others • Discover industrial best practices for explainable ML systems • Use user-centric XAI to bring AI closer to non-technical end users • Address open challenges in XAI using the recommended guidelines Who this book is for This book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. Anyone who is interested in problem-solving using AI will benefit from this book. Foundational knowledge of Python, ML, DL, and data science is recommended. AI/ML experts working with data science, ML, DL, and AI will be able to put their knowledge to work with this practical guide. This book is ideal for you if you're a data and AI scientist, AI/ML engineer, AI/ML product manager, AI product owner, AI/ML researcher, and UX and HCI researcher.
Applied Machine Learning And Deep Learning Architectures And Techniques
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Author : Nitin Liladhar Rane
language : en
Publisher: Deep Science Publishing
Release Date : 2024-10-13
Applied Machine Learning And Deep Learning Architectures And Techniques written by Nitin Liladhar Rane and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-13 with Computers categories.
This book provides an extensive overview of recent advances in machine learning (ML) and deep learning (DL). It starts with a comprehensive introduction to the latest architectural and design practices, with an overview of basic techniques and optimization algorithms and methodologies that are fundamental to modern ML/DL development followed by the tools and frameworks that are driving innovation in ML/DL. The presentation then points to the central position of ML and DL in developing generative AI like ChatGPT. Then look at different industrial applications such as explaining the real-world impacts of each. This includes challenges around corroborate artificial Intelligence (AI), and trustworthy AI, and so on. Finally, the book presents a futuristic vision on the potentials and implications of future ML and DL architectures, making it an ideal guide for researchers, practitioners and industry professionals. This book will be a significant resource for comprehending present advancements, addressing encounter challenges, and traversing the ML and DL landscape in future, making it an indispensable reference for anyone interested in applying these technologies across sectors.
Explainable Ai Foundations Methodologies And Applications
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Author : Mayuri Mehta
language : en
Publisher: Springer Nature
Release Date : 2022-10-19
Explainable Ai Foundations Methodologies And Applications written by Mayuri Mehta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-19 with Technology & Engineering categories.
This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.
Explainable Ai With Python
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Author : Antonio Di Cecco
language : en
Publisher: Springer Nature
Release Date : 2025-08-04
Explainable Ai With Python written by Antonio Di Cecco and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-04 with Computers categories.
This comprehensive book on Explainable Artificial Intelligence has been updated and expanded to reflect the latest advancements in the field of XAI, enriching the existing literature with new research, case studies, and practical techniques. The Second Edition expands on its predecessor by addressing advancements in AI, including large language models and multimodal systems that integrate text, visual, auditory, and sensor data. It emphasizes making complex systems interpretable without sacrificing performance and provides an enhanced focus on additive models for improved interpretability. Balancing technical rigor with accessibility, the book combines theory and practical application to equip readers with the skills needed to apply explainable AI (XAI) methods effectively in real-world contexts. Features: Expansion of the "Intrinsic Explainable Models" chapter to delve deeper into generalized additive models and other intrinsic techniques, enriching the chapter with new examples and use cases for a better understanding of intrinsic XAI models. Further details in "Model-Agnostic Methods for XAI" focused on how explanations differ between the training set and the test set, including a new model to illustrate these differences more clearly and effectively. New section in "Making Science with Machine Learning and XAI" presenting a visual approach to learning the basic functions in XAI, making the concept more accessible to readers through an interactive and engaging interface. Revision in "Adversarial Machine Learning and Explainability" that includes a code review to enhance understanding and effectiveness of the concepts discussed, ensuring that code examples are up-to-date and optimized for current best practices. New chapter on "Generative Models and Large Language Models (LLM)" chapter dedicated to generative models and large language models, exploring their role in XAI and how they can be used to create richer, more interactive explanations. This chapter also covers the explainability of transformer models and privacy through generative models. New "Artificial General Intelligence and XAI" mini-chapter dedicated to exploring the implications of Artificial General Intelligence (AGI) for XAI, discussing how advancements towards AGI systems influence strategies and methodologies for XAI. Enhancements in "Explaining Deep Learning Models" features new methodologies in explaining deep learning models, further enriching the chapter with cutting-edge techniques and insights for deeper understanding.
Interpretability And Explainability In Ai Using Python Decrypt Ai Decision Making Using Interpretability And Explainability With Python To Build Reliable Machine Learning Systems
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Author : Aruna Chakkirala
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-04-15
Interpretability And Explainability In Ai Using Python Decrypt Ai Decision Making Using Interpretability And Explainability With Python To Build Reliable Machine Learning Systems written by Aruna Chakkirala 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 2025-04-15 with Computers categories.
Demystify AI Decisions and Master Interpretability and Explainability Today Key Features● Master Interpretability and Explainability in ML, Deep Learning, Transformers, and LLMs● Implement XAI techniques using Python for model transparency● Learn global and local interpretability with real-world examples Book DescriptionInterpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning, making it easier to debug, validate, and trust. Interpretability and Explainability in AI Using Python takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models. You’ll start with foundational concepts in interpretable machine learning, exploring different model types and their transparency levels. As you progress, you’ll dive into post-hoc methods, feature effect analysis, anchors, and counterfactuals—powerful tools to decode complex models. The book also covers explainability in deep learning, including Neural Networks, Transformers, and Large Language Models (LLMs), equipping you with strategies to uncover decision-making patterns in AI systems. Through hands-on Python examples, you’ll learn how to apply these techniques in real-world scenarios. By the end, you’ll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standards—giving you a competitive edge in the evolving AI landscape. What you will learn● Dissect key factors influencing model interpretability and its different types.● Apply post-hoc and inherent techniques to enhance AI transparency.● Build explainable AI (XAI) solutions using Python frameworks for different models.● Implement explainability methods for deep learning at global and local levels.● Explore cutting-edge research on transparency in transformers and LLMs.● Learn the role of XAI in Responsible AI, including key tools and methods.
Transparent Ai Defenses A Random Forest Approach Augmented By Shap For Malware Threat Evaluation
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Author : Manas Yogi
language : en
Publisher: GRIN Verlag
Release Date : 2025-10-01
Transparent Ai Defenses A Random Forest Approach Augmented By Shap For Malware Threat Evaluation written by Manas Yogi and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-01 with Computers categories.
Master's Thesis from the year 2025 in the subject Computer Science - Internet, New Technologies, grade: A, , course: M.Tech, language: English, abstract: The rapid evolution of malware poses an ever-growing challenge to cybersecurity professionals and organizations worldwide. As malicious software becomes more sophisticated, traditional detection methods often fall short, necessitating advanced solutions that not only identify threats but also provide clear explanations for their predictions. This book, Transparent AI Defenses: A Random Forest Approach Augmented by SHAP for Malware Threat Evaluation, emerges from this critical need, offering a comprehensive exploration of an explainable artificial intelligence (XAI) framework tailored for malware analysis. Our journey began with a desire to bridge the gap between the predictive power of machine learning and the interpretability demanded by security experts. The Random Forest algorithm, known for its robustness, serves as the backbone of our approach, while SHAP (SHapley Additive exPlanations) enhances it by delivering actionable insights into feature importance.
Explainable Ai Interpreting Explaining And Visualizing Deep Learning
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Author : Wojciech Samek
language : en
Publisher: Springer Nature
Release Date : 2019-09-10
Explainable Ai Interpreting Explaining And Visualizing Deep Learning written by Wojciech Samek and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-10 with Computers categories.
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
Ieee International Symposium On Intelligent Control 1999
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Author : IEEE Control Systems Society Staff
language : en
Publisher: IEEE Standards Office
Release Date : 1999-09
Ieee International Symposium On Intelligent Control 1999 written by IEEE Control Systems Society Staff and has been published by IEEE Standards Office this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-09 with Technology & Engineering categories.
This volume contains the proceedings of the 1999 IEEE International Symposium on Intelligent Control. The wide variety of topics covered include; timed discrete event systems; learning, genetic and fuzzy systems; emotions in psychology and neural networks; and a panel discussion on autonomy.
Explainable Ai For Practitioners
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Author : Michael Munn
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-10-31
Explainable Ai For Practitioners written by Michael Munn and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-31 with Computers categories.
Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does. Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow. This essential book provides: A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs Tips and best practices for implementing these techniques A guide to interacting with explainability and how to avoid common pitfalls The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace
Quantitative Asset Management Factor Investing And Machine Learning For Institutional Investing
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Author : Michael Robbins
language : en
Publisher: McGraw Hill Professional
Release Date : 2023-06-24
Quantitative Asset Management Factor Investing And Machine Learning For Institutional Investing written by Michael Robbins and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-24 with Business & Economics categories.
Augment your asset allocation strategy with machine learning and factor investing for unprecedented returns and growth Whether you’re managing institutional portfolios or private wealth, Quantitative Asset Management will open your eyes to a new, more successful way of investing—one that harnesses the power of big data and artificial intelligence. This innovative guide walks you through everything you need to know to fully leverage these revolutionary tools. Written from the perspective of a seasoned financial investor making use of technology, it details proven investing methods, striking a rare balance between providing important technical information without burdening you with overly complex investing theory. Quantitative Asset Management is organized into four thematic sections: Part I reveals invaluable lessons for planning and governance of investment decision-making. Part 2 discusses quantitative financial modeling, covering important topics like overfitting, mitigating unrealistic assumptions, managing substitutions, enhancing minority classes, and missing data imputation. Part 3 shows how to develop a strategy into an investment product, including the alpha models, risk models, implementation, backtesting, and cost optimization. Part 4 explains how to measure performance, learn from mistakes, manage risk, and survive financial tragedies. With Quantitative Asset Management, you have everything you need to build your awareness of other markets, ask the right questions and answer them effectively, and drive steady profits even through times of great uncertainty.