Neurale net og fuzzy systemer

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If youre comfortable with Python and its libraries, including pandas and scikit-learn, youll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics.Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.Youll find recipes for:Vectors, matrices, and arraysHandling numerical and categorical data, text, images, and dates and timesDimensionality reduction using feature extraction or feature selectionModel evaluation and selectionLinear and logical regression, trees and forests, and k-nearest neighborsSupport vector machines (SVM), nave Bayes, clustering, and neural networksSaving and loading trained models

The concept of deep machine learning is easier to understand by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the cycle-to-cycle variations.

Connectomic Medicine: A Guide to Brain AI in Treatment Decision Planning examines how to apply connectomics to clinical medicine, including discussions on techniques, applications, novel ideas, and in case examples that highlight the state-of-the-art. Written by pioneers, this volume serves as the foundation for all neuroscience clinicians/researchers venturing into the field of AI medicine, its realistic applications, and how to integrate AI connectomics into clinical practice. With widespread applications in neurology, neurosurgery and psychiatry, this book is appropriate for anyone interested in cerebral network anatomy, imaging techniques, and insights into this emerging field.

Fuzzy logic principles, practices, and real-world applicationsThis hands-on guide offers clear explanations of fuzzy logic along with practical applications and real-world examples. Written by an award-winning engineer, Fuzzy Logic: Applications in Artificial Intelligence, Big Data, and Machine Learning is aimed at improving competence and motivation in students and professionals alike.Inside, you will discover how to apply fuzzy logic in the context of pervasive digitization and big data across emerging technologies which require a very different man-machine relationship than the ones previously used in engineering, science, economics, and social sciences. Applications covered include intelligent energy systems with demand response, smart homes, electrification of transportation, supply chain efficiencies, smart cities, e-commerce, education, healthcare, and decarbonization.Serves as a classroom guide and as an on-the-job resourceAncillaries include a sample syllabus, test sets with answer keys, and additional self-study resources for studentsWritten by an expert in the field and experienced author

The rise in living standards increases the expectation of people in almost every field. At the forefront is health. Over the past few centuries, there have been major developments in healthcare. Medical device technology and developments in artificial intelligence (AI) are among the most important ones. The improving technology and our ability to harness the technology effectively by means such as AI have led to unprecedented advances, resulting in early diagnosis of diseases. AI algorithms enable the fast and early evaluation of images from medical devices to maximize the benefits. While developments in the field of AI were quickly adapted to the field of health, in some cases this contributed to the formation of innovative artificial intelligence algorithms. Today, the most effective artificial intelligence method is accepted as deep learning. Convolutional neural network (CNN) architectures are deep learning algorithms used for image processing. This book contains applications of CNN methods. The content is quite extensive, including the application of different CNN methods to various medical image processing problems. Readers will be able to analyze the effects of CNN methods presented in the book in medical applications.

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 needsTips and best practices for implementing these techniquesA guide to interacting with explainability and how to avoid common pitfallsThe knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systemsAdvice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text dataExample implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace

This book helps to enhance the application of fuzzy logic optimization in the areas of science and engineering. It includes implementation and models and paradigms, such as path planning and routing design for different wireless networks, organization behavior strategies models, and so forth. It also:Explains inventory control management, uncertainties management, loss minimization, game optimization, data analysis and prediction, different decision-makingsystem and management, and so forthDescribes applicability of fuzzy optimization techniques in areas of science and managementResolves several issues based on uncertainty using member functionHelps map different problems based on mathematical modelsIncludes issues and problems based on linear and nonlinear optimizationsFocuses on management science such as manpower management and inventory planningThis book is aimed at researchers and graduate students in signal processing, power systems, systems and industrial engineering, and computer networks.

This comprehensive compendium designs deep neural network models and systems for intelligent analysis of fundus imaging. In response to several blinding fundus diseases such as Retinopathy of Prematurity (ROP), Diabetic Retinopathy (DR) and Macular Edema (ME), different image acquisition devices and fundus image analysis tasks are elaborated.From the actual fundus disease analysis tasks, various deep neural network models and experimental results are constructed and analyzed. For each task, an actual system for clinical application is developed.This useful reference text provides theoretical and experimental reference basis for AI researchers, system engineers of intelligent medicine and ophthalmologists.

Creativity is essential to being a successful programmer. Each chapter in The Creative Programmer introduces you to a new theme of creativity that is derived from scientifically sound research. Discover the importance of communication, how constraints can make you more creative, methods to improve your critical thinking, and more. Short stories, examples, and exercises will help you understand each new idea and clearly demonstrate how you can apply them to programming. You will even be able to track your progress against a scientifically validated Creative Programming Problem Solving Test! Along the way, you will enjoy examples and stories that show what makes creative technical geniuses tick. About the reader For programmers of all experience and skill levels.

This book demonstrates the core concepts of deep learning algorithms that, using diagrams, data tables, and examples, are especially useful for deep learning based human cancer diagnostics.

This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. It presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization, and hardware-level instruction acceleration.

About the book In Graph Neural Networks in Action you will create deep learning models that are perfect for working with interconnected graph data. Start with a comprehensive introduction to graph data's unique properties. Then, dive straight into building real-world models, including GNNs that can generate node embeddings from a social network, recommend eCommerce products, and draw insights from social sites. This comprehensive guide contains coverage of the essential GNN libraries, including PyTorch Geometric, DeepGraph Library, and Alibaba's GraphScope for training at scale. About the reader For Python programmers familiar with machine learning and the basics of deep learning.

Based on his popular AI Insider series and podcasts, this vital book by industry thought leader and global expert, Dr. Lance Eliot, provides pioneering advances for the advent of AI self-driving driverless cars. Included are insights about the practical application of Artificial Intelligence and Machines Learning.

World renowned AI self-driving car expert, Dr. Lance Eliot, provides the latest top trends in AI Machine Learning and self-driving cars. Merging together his insights about Autonomous Vehicles (AV) and self-driving robot cars, he covers the high-tech aspects along with business and societal impacts. For access to his podcasts, see his web site: www.ai-selfdriving-cars.guru

A vital book by industry thought leader and global AI expert, Dr. Lance Eliot, and based on his popular AI Insider series and podcasts, this fascinating book provides key advances and prognosis for the advent of AI self-driving driverless autonomous cars. Included too are keen insights about the practical application of Artificial Intelligence (AI) and Machines Learning (ML).

Artificial Intelligence (AI) is becoming a tremendous disruptive force in how our world works. One area that will have an incredible impact on us all is the advent of driverless self-driving cars. This ground breaking book covers key high-tech advances, along with the business raminifications of this emerging transformation. Dr. Lance B. Eliot is the Executive Director of the AI Cybernetic Self-Driving Car Institute, develops AI self-driving car systems, and he also writes a popular AI Insider column and produces a twice weekly podcast. www.ai-selfdriving-cars.guru