k b saha (15 Ergebnisse)

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

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

Soft cover. Zustand: Very Good. No Jacket. 2nd Edition. Very Good in Softcover. 486pp 8vo. Pink spine with blue titles.

Zustand: Used - Very Good. VG paperback. Reprint of 2nd revised edition, 1996. Pages generally clean; slight wear to cover. Used - Very Good. VG paperback.

Taschenbuch. Zustand: Neu. Neuware - 'Economics Of Rural Bengal,' written by K. B. Saha and originally published in 1929, offers a detailed examination of the economic conditions prevailing in rural Bengal during the early 20th century. This historical study provides insights into the agrarian structure, agricultural practices, and socio-economic factors that shaped the lives of rural communities. Saha's work delves into the intricacies of land ownership, farming techniques, and the economic challenges faced by the rural population.

Buch. Zustand: Neu. Neuware.

Buch. Zustand: Neu. Neuware - Biomechanics: Basic Fundamentals offers an accessible yet in-depth introduction to the study of movement and forces in biological systems. Perfect for students, researchers, and professionals in fields like kinesiology, physical therapy, and sports science, this book covers the core principles of biomechanics and their real-world applications. Key Features: undamental Concepts: Explains the basic principles of mechanics, including force, motion, and energy, as they apply to biological systems. Human Movement Analysis: Covers the mechanics of joint motion, muscle forces, and the efficiency of human movements in daily life and sports. Structural Biomechanics: Explores the design and function of bones, muscles, tendons, and ligaments, focusing on their mechanical properties. Biomechanics in Action: Discusses practical applications in rehabilitation, sports performance, ergonomics, and injury prevention. Mathematical Models and Calculations: Introduces essential equations and models used to analyze human movement and mechanical forces. Case Studies and Practical Examples: Features real-world applications and case studies to demonstrate the theory in practice. Illustrations and Visual Aids: Provides clear diagrams, charts, and images to enhance understanding of complex biomechanics concepts. Student-Friendly Structure: Includes review questions, examples, and problem sets to help reinforce learning and exam preparation.

Zustand: New. In.

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.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.

Hardcover. Zustand: Brand New. 256 pages. 6.69x9.44x0.55 inches. In Stock.

Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.

Zustand: New. 2020. Hardcover. . . . . . Books ship from the US and Ireland.