Practical Explainable Ai Using Python
DOWNLOAD
Download Practical Explainable Ai Using Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Practical Explainable Ai Using Python book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Practical Explainable Ai Using Python
DOWNLOAD
Author : Pradeepta Mishra
language : en
Publisher:
Release Date : 2022
Practical Explainable Ai Using Python written by Pradeepta Mishra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with 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 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. You will: 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.
Explainable Ai With Python
DOWNLOAD
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.
Hands On Explainable Ai Xai With Python
DOWNLOAD
Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-31
Hands On Explainable Ai Xai With Python written by Denis Rothman 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 2020-07-31 with Computers categories.
Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key FeaturesLearn explainable AI tools and techniques to process trustworthy AI resultsUnderstand how to detect, handle, and avoid common issues with AI ethics and biasIntegrate fair AI into popular apps and reporting tools to deliver business value using Python and associated toolsBook Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learnPlan for XAI through the different stages of the machine learning life cycleEstimate the strengths and weaknesses of popular open-source XAI applicationsExamine how to detect and handle bias issues in machine learning dataReview ethics considerations and tools to address common problems in machine learning dataShare XAI design and visualization best practicesIntegrate explainable AI results using Python modelsUse XAI toolkits for Python in machine learning life cycles to solve business problemsWho this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications
Explainable Ai Recipes
DOWNLOAD
Author : Pradeepta Mishra
language : en
Publisher: Apress
Release Date : 2023-02-09
Explainable Ai Recipes 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 2023-02-09 with Computers categories.
Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining machine learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses. What You Will Learn Create code snippets and explain machine learning models using Python Leverage deep learning models using the latest code with agile implementations Build, train, and explain neural network models designed to scale Understand the different variants of neural network models Who This Book Is For AI engineers, data scientists, and software developers interested in XAI
Interpretable Ai
DOWNLOAD
Author : Ajay Thampi
language : en
Publisher: Simon and Schuster
Release Date : 2022-07-05
Interpretable Ai written by Ajay Thampi 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 2022-07-05 with Computers categories.
AI doesn't have to be a black box. These practical techniques help shine a light on your model's mysterious inner workings. Make your AI more transparent, and you'll improve trust in your results, combat data leakage and bias, and ensure compliance with legal requirements. Interpretable AI opens up the black box of your AI models. It teaches cutting-edge techniques and best practices that can make even complex AI systems interpretable. Each method is easy to implement with just Python and open source libraries. You'll learn to identify when you can utilize models that are inherently transparent, and how to mitigate opacity when your problem demands the power of a hard-to-interpret deep learning model.
Interpretability And Explainability In Ai Using Python Decrypt Ai Decision Making Using Interpretability And Explainability With Python To Build Reliable Machine Learning Systems
DOWNLOAD
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.
Applied Machine Learning Explainability Techniques
DOWNLOAD
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.
Explainable Ai For Practitioners
DOWNLOAD
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
Artificial Intelligence For Complete Beginners
DOWNLOAD
Author : Richard D Crowley
language : en
Publisher: Independently Published
Release Date : 2025-02-27
Artificial Intelligence For Complete Beginners written by Richard D Crowley and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-27 with Computers categories.
This book provides a comprehensive and practical guide to Artificial Intelligence, designed to equip readers with the knowledge and skills necessary to understand and build AI applications.1 From foundational machine learning algorithms to advanced deep learning techniques, the text covers a wide range of topics with a focus on hands-on implementation using Python. It emphasizes ethical considerations, emerging trends like Explainable AI and Generative AI, and offers practical guidance on deploying AI models in real-world scenarios. The appendices serve as valuable resources, covering essential Python libraries, AI terminology, datasets, troubleshooting, and the mathematical underpinnings of key algorithms, making it an ideal resource for both beginners and those seeking to deepen their understanding of AI.
Mastering Machine Learning With Tensorflow Pytorch And Scikit Learn
DOWNLOAD
Author : Dr Benjamin Neudorf
language : en
Publisher: Independently Published
Release Date : 2025-08-27
Mastering Machine Learning With Tensorflow Pytorch And Scikit Learn written by Dr Benjamin Neudorf and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-27 with Computers categories.
Unlock the Power of Machine Learning-No Experience Required Are you fascinated by artificial intelligence but feel overwhelmed by the jargon, complexity, or sheer scale of machine learning? Do you dream of building intelligent systems, but worry you lack the background, confidence, or mathematical skills to get started? You're not alone-and this book is for you. Mastering Machine Learning with TensorFlow, PyTorch, and Scikit-Learn: A Practical Python Guide is your friendly, step-by-step introduction to modern machine learning. Whether you're a complete beginner or a curious developer, you'll discover how easy-and fun-machine learning can be with the right guide at your side. What You'll Find Inside: Beginner-Friendly Approach: No prior experience in machine learning, statistics, or advanced Python required. Every concept is broken down into plain language and hands-on examples, guiding you gently from your very first line of code to complete, working projects. Confidence-Building Tutorials: Learn by doing with real-world datasets, detailed walkthroughs, and plenty of practical exercises-so you'll never feel lost or left behind. Three Powerful Frameworks, One Book: Master the essentials of TensorFlow, PyTorch, and Scikit-Learn-the leading Python libraries used by top companies and research labs worldwide. Real-World Projects: Go beyond theory. Build your own machine learning models for regression, classification, image recognition, and more, using code you can run, adapt, and expand for your own ideas. Supportive, Encouraging Voice: Mistakes are normal-and often the best teachers. Throughout this book, you'll find troubleshooting tips, gentle encouragement, and guidance that celebrates your progress and every small win. Key Benefits: Gain a clear, practical understanding of the entire machine learning workflow-from data preparation to model deployment. Develop strong Python skills while building confidence with professional tools and libraries. Understand core concepts like neural networks, deep learning, transfer learning, and explainable AI without the intimidation. Apply your new skills immediately to real problems, unlocking doors in tech, business, research, and beyond. Why This Book Stands Out: Step-by-step, project-based lessons perfect for absolute beginners. Friendly explanations that demystify machine learning and artificial intelligence. Practical, working code for every topic-no more guesswork or copying from unreliable sources. Written by an experienced educator who remembers what it feels like to start from scratch. Ready to Begin Your Machine Learning Journey? You don't need a PhD or years of experience. All you need is curiosity, determination, and the right companion to guide you. Start reading Mastering Machine Learning with TensorFlow, PyTorch, and Scikit-Learn today-and take your first confident step toward a future in AI. Don't just learn machine learning-master it, one step at a time. Scroll up and get your copy now!