applications machine learning deep (103 Ergebnisse)

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 112 | Sprache: Englisch | Produktart: Bücher.

Zustand: New. In.

Taschenbuch. Zustand: Neu. Big Data, Machine, and Deep Learning | Recent Progress, Key Applications, and Future Directions | Rajesh Kumar Mishra (u. a.) | Taschenbuch | Englisch | 2025 | GRIN Verlag | EAN 9783389122501 | Verantwortliche Person für die EU: preigu, Ansas Meyer, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Zustand: New. In.

Hardcover. Zustand: New. New hardcover with no dust jacket. An unread/unopened copy. May have very minor rubbing and scuffing from shelf.; 100% Satisfaction Guaranteed! Ships same or next business day!

Zustand: New. In.

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. In.

Taschenbuch. Zustand: Neu. Neuware -This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning toaddress a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 124 pp. Englisch.

Zustand: New. pages cm.

Zustand: New.

Zustand: New.

Zustand: New. pages cm.

Zustand: New. In.

Taschenbuch. Zustand: Neu. Neuware -A book that covers different machine learning and deep learning algorithms specifically for lung cancer would be an excellent resource for researchers, medical professionals, and data scientists interested in leveraging artificial intelligence for medical diagnosis and treatment. While I can't provide a specific book title, I can suggest the key topics and areas such a book might cover: Introduction to Lung Cancer: The book may start with an overview of lung cancer, including its types, causes, diagnosis methods, and current treatment options. Machine Learning Fundamentals: It may cover fundamental concepts of machine learning, including supervised, unsupervised, and semi-supervised learning, as well as evaluation metrics commonly used in medical applications.Data Preprocessing and Feature Engineering: The book may discuss techniques for preprocessing medical imaging data, such as CT scans or X-rays, including image normalization, noise reduction, and feature extraction.Classification Algorithms: It would likely delve into various classification algorithms used for lung cancer detection and diagnosis, such as support vector machines (SVM), random forests, decision trees.Books on Demand GmbH, Überseering 33, 22297 Hamburg 184 pp. Englisch.

Zustand: New.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 180 | Sprache: Englisch | Produktart: Bücher.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere.Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning;Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes;Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 296 | Sprache: Englisch | Produktart: Bücher.

Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 244 | Sprache: Englisch | Produktart: Bücher.

Paperback. Zustand: Brand New. 212 pages. 8.66x5.91x0.48 inches. In Stock.

Zustand: New.

Zustand: New. In.

Zustand: New. In.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance.This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance.This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.

Zustand: New.