michelucci umberto (23 Ergebnisse)

Paperback. Zustand: Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 0.94.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In.

Zustand: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.

Zustand: New. 2019. 1st ed. paperback. . . . . . Books ship from the US and Ireland.

Zustand: New.

Paperback. Zustand: Brand New. 285 pages. 9.00x6.00x0.75 inches. In Stock.

Zustand: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

Zustand: New.

Zustand: New. In.

Hardcover. Zustand: Brand New. 150 pages. 9.26x6.11x9.49 inches. In Stock.

Zustand: New. 2022. 2nd ed. Paperback. . . . . . Books ship from the US and Ireland.

Zustand: NEW.

Hardcover. Zustand: Sehr gut. Gebraucht - Sehr gut - ungelesen,als Mängelexemplar gekennzeichnet, mit leichten Mängeln an Schnitt oder Einband durch Lager- oder Transportschaden -This book offers researchers and practitioners a concise and accessible guide to the essential concepts in statistics, emphasizing their proper application. It encourages readers to delve deeper into the fascinating field of statistics, a branch of mathematics that enhances our understanding of the world around us. Designed to provide enough material for a short introductory course, Statistics for Scientists caters to students at all levels. It emphasizes real-world applications, providing scientists with the tools they need to conduct more reliable and valid studies, ultimately contributing to the advancement of scientific knowledge. Learn to interpret statistical results accurately and draw meaningful conclusions from your data, significantly contributing to the advancement of scientific knowledge.Structured to deliver a clear overview of statistics and data analysis for scientific research, the book begins with fundamental concepts, including random variables, outcome spaces, and the distinction between descriptive and inferential statistics. It then explores data types, measures of central tendency, dispersion, and position. The discussion continues with an examination of outliers and various methods for identifying them. As the chapters progress, more complex topics such as distributions, hypothesis testing, and regression analysis are introduced in a step-by-step manner. This structure makes the book suitable for readers ranging from beginners to those seeking a quick refresher.The author has selected key concepts that anyone interested in using statistics should be familiar with. Some topics, such as hypothesis testing, are covered briefly; a more comprehensive treatment would require a stronger background in statistics and mathematics (such as calculus). With pedagogical elements that include text boxes with Definitions, Examples, and Warnings, this book introduces the necessary concepts of statistics for scientists described in a short and concise way, enriched with tips and rigorous explanations. This book is an invaluable resource for scientists seeking to improve their data analysis skills and contribute to the growing body of scientific knowledge through rigorous and reliable research.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 191 pp. Englisch.

Zustand: New.

Hardcover. Zustand: Brand New. 150 pages. 9.26x6.11x9.49 inches. In Stock.

Taschenbuch. Zustand: Neu. Neuware -Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow.Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models.Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 304 pp. Englisch.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers researchers and practitioners a concise and accessible guide to the essential concepts in statistics, emphasizing their proper application. It encourages readers to delve deeper into the fascinating field of statistics, a branch of mathematics that enhances our understanding of the world around us. Designed to provide enough material for a short introductory course, Statistics for Scientists caters to students at all levels. It emphasizes real-world applications, providing scientists with the tools they need to conduct more reliable and valid studies, ultimately contributing to the advancement of scientific knowledge.Learn to interpret statistical results accurately and draw meaningful conclusions from your data, significantly contributing to the advancement of scientific knowledge.Structured to deliver a clear overview of statistics and data analysis for scientific research, the book begins with fundamental concepts, including random variables, outcome spaces, and the distinction between descriptive and inferential statistics. It then explores data types, measures of central tendency, dispersion, and position. The discussion continues with an examination of outliers and various methods for identifying them. As the chapters progress, more complex topics such as distributions, hypothesis testing, and regression analysis are introduced in a step-by-step manner. This structure makes the book suitable for readers ranging from beginners to those seeking a quick refresher.The author has selected key concepts that anyone interested in using statistics should be familiar with. Some topics, such as hypothesis testing, are covered briefly; a more comprehensive treatment would require a stronger background in statistics and mathematics (such as calculus). With pedagogical elements that include text boxes with Definitions, Examples, and Warnings, this book introduces the necessary concepts of statistics for scientists described in a short and concise way, enriched with tips and rigorous explanations.This book is an invaluable resource for scientists seeking to improve their data analysis skills and contribute to the growing body of scientific knowledge through rigorous and reliable research.

Buch. Zustand: Neu. Neuware -This book is for individuals with a scientific background who aspire to apply machine learning within various natural science disciplines¿such as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous mathematical integrity. For readers more versed in mathematics, the book includes advanced sections that are not prerequisites for the initial reading. It ensures concepts are clearly defined and theorems are proven where it's pertinent. Machine learning transcends the mere implementation and training of algorithms; it encompasses the broader challenges of constructing robust datasets, model validation, addressing imbalanced datasets, and fine-tuning hyperparameters. These topics are thoroughly examined within the text, along with the theoretical foundations underlying these methods. Rather than concentrating on particular algorithms this book focuses on the comprehensive concepts and theories essential for their application. It stands as an indispensable resource for any scientist keen on integrating machine learning effectively into their research.Numerous texts delve into the technical execution of machine learning algorithms, often overlooking the foundational concepts vital for fully grasping these methods. This leads to a gap in using these algorithms effectively across diverse disciplines. For instance, a firm grasp of calculus is imperative to comprehend the training processes of algorithms and neural networks, while linear algebra is essential for the application and efficient training of various algorithms, including neural networks. Absent a solid mathematical base, machine learning applications may be, at best, cursory, or at worst, fundamentally flawed. This book lays the foundation for a comprehensive understanding of machine learning algorithms and approaches.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 268 pp. Englisch.

Paperback. Zustand: Brand New. 410 pages. 9.75x7.00x1.00 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is for individuals with a scientific background who aspire to apply machine learning within various natural science disciplines-such as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous mathematical integrity. For readers more versed in mathematics, the book includes advanced sections that are not prerequisites for the initial reading. It ensures concepts are clearly defined and theorems are proven where it's pertinent. Machine learning transcends the mere implementation and training of algorithms; it encompasses the broader challenges of constructing robust datasets, model validation, addressing imbalanced datasets, and fine-tuning hyperparameters. These topics are thoroughly examined within the text, along with the theoretical foundations underlying these methods. Rather than concentrating on particular algorithms this book focuses on the comprehensive concepts and theories essential for their application. It stands as an indispensable resource for any scientist keen on integrating machine learning effectively into their research.Numerous texts delve into the technical execution of machine learning algorithms, often overlooking the foundational concepts vital for fully grasping these methods. This leads to a gap in using these algorithms effectively across diverse disciplines. For instance, a firm grasp of calculus is imperative to comprehend the training processes of algorithms and neural networks, while linear algebra is essential for the application and efficient training of various algorithms, including neural networks. Absent a solid mathematical base, machine learning applications may be, at best, cursory, or at worst, fundamentally flawed. This booklays the foundation for a comprehensive understandingof machine learning algorithms and approaches.

Hardcover. Zustand: Brand New. 266 pages. 9.25x6.10x9.49 inches. In Stock.