Gør som tusindvis af andre bogelskere
Tilmeld dig nyhedsbrevet og få gode tilbud og inspiration til din næste læsning.
Ved tilmelding accepterer du vores persondatapolitik.Du kan altid afmelde dig igen.
Machine Learning in Earth, Environmental and Planetary Sciences: Theoretical and Practical Applications is a practical guide on implementing different variety of extreme learning machine algorithms to Earth and environmental data. The book provides guided examples using real-world data for numerous novel and mathematically detailed machine learning techniques that can be applied in Earth, environmental, and planetary sciences, including detailed MATLAB coding coupled with line-by-line descriptions of the advantages and limitations of each method. The book also presents common postprocessing techniques required for correct data interpretation. This book provides students, academics, and researchers with detailed understanding of how machine learning algorithms can be applied to solve real case problems, how to prepare data, and how to interpret the results.
This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward approach guided by real-world examples in areas such as health informatics, information technology, and business analytics. The book will help readers understand the various key algorithms, major software tools, and their applications. Moreover, through examples from the healthcare and business analytics fields, it demonstrates how and when ML can help them make better decisions in their disciplines.The book is chiefly intended for undergraduate and graduate students who are taking an introductory course in machine learning. It will also benefit data analysts and anyone interested in learning ML approaches.
Solve SEO problems using data science. This hands-on book is packed with Python code and data science techniques to help you generate data-driven recommendations and automate the SEO workload. This book is a practical, modern introduction to data science in the SEO context using Python. With social media, mobile, changing search engine algorithms, and ever-increasing expectations of users for super web experiences, too much data is generated for an SEO professional to make sense of in spreadsheets. For any modern-day SEO professional to succeed, it is relevant to find an alternate solution, and data science equips SEOs to grasp the issue at hand and solve it. From machine learning to Natural Language Processing (NLP) techniques, Data-Driven SEO with Python provides tried and tested techniques with full explanations for solving both everyday and complex SEO problems.This book is ideal for SEO professionals who want to take their industry skills to the next level and enhance their business value, whether they are a new starter or highly experienced in SEO, Python programming, or both. What You'll LearnSee how data science works in the SEO contextThink about SEO challenges in a data driven wayApply the range of data science techniques to solve SEO issuesUnderstand site migration and relaunches areWho This Book Is ForSEO practitioners, either at the department head level or all the way to the new career starter looking to improve their skills. Readers should have basic knowledge of Python to perform tasks like querying an API with some data exploration and visualization.
Artificial intelligence (AI) is a concept, whose meaning and perception has changed considerably over the last decades. Starting off with individual and purely theoretical research efforts in the 1950s, AI has grown into a fully developed research field of modern times and may arguably emerge as one of the most important technological advancements of mankind. Despite these rapid technological advancements, some key questions revolving around the matter of transparency, interpretability and explainability of an AI's decision-making remain unanswered. Thus, a young research field coined with the general term Explainable AI (XAI) has emerged from increasingly strict requirements for AI to be used in safety critical or ethically sensitive domains. An important research branch of XAI is to develop methods that help to facilitate a deeper understanding for the learned knowledge of artificial neural systems. In this book, a series of scientific studies are presented that shed light on how to adopt an empirical neuroscience inspired approach to investigate a neural network's learned representation in the same spirit as neuroscientific studies of the brain.
This book provides a comprehensive explanation of precision (i.e., personalized) healthcare and explores how it can be advanced through artificial intelligence (AI) and other data-driven technologies. From improving the diagnosis, treatment, and monitoring of many medical conditions to the effective implementation of precise patient care, this book will help you understand datasets produced from digital health technologies and IoT and teach you how to employ analytical methods such as convolutional neural networks and deep learning to analyze that data. Yoüll also see how this data-driven approach can enhance and democratize value-based healthcare delivery. Additionally, yoüll learn how the convergence of AI and precision health is revolutionizing healthcare, including some of the most difficult challenges facing precision medicine, such as ethics, bias, privacy, and health equity. Precision Health and Artificial Intelligence provides the groundwork for clinicians, engineers, bioinformaticians, and healthcare enthusiasts to apply AI to healthcare.What You Will LearnUnderstand the components required to facilitate precision health and personalized careApply and implement precision health systemsOvercome the challenges of delivering precision healthcare at scaleReconcile ethical and moral implications of delivering precision healthcareGain insight into the hurdles providers face while implementing precision healthcare Who This Book Is ForHealthcare professionals, clinicians, engineers, bioinformaticians, chief information officers (CIOs), and students
This book gives readers a practical introduction to machine learning and sensing techniques, their design and ultimately specific applications that could improve food production. It shows how these sensing and computing systems are suitable for process implementation in food factories.
This book constitutes the refereed proceedings of the 18th International Conference on Frontiers in Handwriting Recognition, ICFHR 2022, which took place in Hyderabad, India, during December 4-7, 2022.The 36 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 61 submissions. The contributions were organized in topical sections as follows: Historical Document Processing; Signature Verification and Writer Identification; Symbol and Graphics Recognition; Handwriting Recognition and Understanding; Handwriting Datasets and Synthetic Handwriting Generation; Document Analysis and Processing.
This book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid pace.Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader's learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge.This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional prerequisites.
This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization.The book begins by introducing different Bayesian Optimization (BO) techniques, covering both commonly used tools and advanced topics. It follows a ¿develop from scratch¿ method using Python, and gradually builds up to more advanced libraries such as BoTorch, an open-source project introduced by Facebook recently. Along the way, yoüll see practical implementations of this important discipline along with thorough coverage and straightforward explanations of essential theories. This book intends to bridge the gap between researchers and practitioners, providing both with a comprehensive, easy-to-digest, and useful reference guide. After completingthis book, you will have a firm grasp of Bayesian optimization techniques, which yoüll be able to put into practice in your own machine learning models.What You Will LearnApply Bayesian Optimization to build better machine learning modelsUnderstand and research existing and new Bayesian Optimization techniquesLeverage high-performance libraries such as BoTorch, which offer you the ability to dig into and edit the inner workingDig into the inner workings of common optimization algorithms used to guide the search process in Bayesian optimizationWho This Book Is ForBeginner to intermediate level professionals in machine learning, analytics or other roles relevant in data science.
Machine Learning in Manufacturing: Quality 4.0 and the Zero Defects Vision reviews process monitoring based on machine learning algorithms and the technologies of the fourth industrial revolution and proposes Learning Quality Control (LQC), the evolution of Statistical Quality Control (SQC). This book identifies 10 big data issues in manufacturing and addresses them using an ad-hoc, 5-step problem-solving strategy that increases the likelihood of successfully deploying this Quality 4.0 initiative. With two case studies using structured and unstructured data, this book explains how to successfully deploy AI in manufacturing and how to move quality standards forward by developing virtually defect-free processes. This book enables engineers to identify Quality 4.0 applications and manufacturing companies to successfully implement Quality 4.0 practices.
"A gentle but detailed introduction to some of the algorithmic ideas behind machine learning"--
Erstellen Sie einen Chatbot mit der Microsoft Conversational AI-Plattform. In diesem Buch lernen Sie Schritt für Schritt, wie Sie Zeit und Geld sparen können, indem Sie Chatbots in die Strategie Ihres Unternehmens integrieren. Sie werden lernen, wie Sie jede Phase der Entwicklung beherrschen, von der Zusammenarbeit an einem Chatbot in einem End-to-End-Szenario über die erste Mock-up-Phase bis hin zur Einsatz- und Bewertungsphase. Microsoft hat ein Cloud-Service-Ökosystem für die Ausführung von künstlichen Intelligenz-Workloads in öffentlichen Cloud-Szenarien und eine robuste KI-Plattform aufgebaut, die eine breite Palette von Diensten für konversationelle künstliche Intelligenz-Lösungen wie Chatbots bietet. Die Entwicklung eines Chatbots erfordert nicht nur Programmierkenntnisse von Entwicklern, sondern auch besondere Überlegungen, einschließlich des Inputs von Geschäftsinteressenten wie Fachexperten und Power-Usern. Sie werden anhand von Beispielen lernen, wie Sie eine Reihe vonTools und Diensten nutzen können, um die Kluft zwischen Unternehmen und Technik zu überbrücken. Sie werden lernen, wie Sie Geschäftsanforderungen erfolgreich in umsetzbare IT- und technische Anforderungen umwandeln können. Sie lernen den Bot Framework Composer kennen, der es Power-Usern ermöglicht, die Erstellung eines Chatbots zu initiieren, der dann an das Entwicklungsteam weitergegeben werden kann, um durch Code weitere Funktionen hinzuzufügen. Der Prozess der Aufteilung der Implementierungsaufgaben und des Arbeitsaufwands zwischen Power-Usern, die einen Low-Code- oder No-Code-Ansatz verwenden, und Entwicklern, die die erweiterten Funktionen für den Chatbot entwickeln, wird ebenfalls behandelt.Was Sie lernen werden:Verstehen Sie Microsofts umfassendes KI-Ökosystem und seine Dienste und LösungenErkennen Sie, welche Lösungen und Dienste in jedem Geschäftsszenario angewendet werden solltenEntdecken Sie No-Code-/Low-Code-Ansätze für die Erstellung von ChatbotsEntwickeln Sie Chatbots unter Verwendung des Conversational AI StacksRichten Sie Geschäft und Entwicklung für verbesserte Chatbot-Ergebnisse und kürzere Markteinführungszeiten ausDieses Buch richtet sich an Entwickler und Power-User, die Chatbots erstellen möchten. Ein Verständnis für die Grundprinzipien des Programmierens moderner Webanwendungen (.NET oder JavaScript) wird vorausgesetzt.
Tilmeld dig nyhedsbrevet og få gode tilbud og inspiration til din næste læsning.
Ved tilmelding accepterer du vores persondatapolitik.