K b saha (47 Ergebnisse)

Paperback or Softback. Zustand: New. Economics Of Rural Bengal. Book.

Zustand: New.

Paperback. Zustand: New.

Zustand: New.

Paperback. Zustand: Good. 8vo, paperback. Good condition. Exterior browned/smudged w/ moderate spine lean, small chips at spine ends; 1/2-inch curl/crinkle to pg corners & tiny marginal note on 1 pg, interior otherwise clean, binding firm. 138 pp., illus., tables, bibliography.

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

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

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.…

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Buch. Zustand: Neu. Neuware.

Paperback. Zustand: New.

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.…

Hardcover. Zustand: Very Good. 2018 Springer hardcover edition. Light reading wear else very good condition. Contents 1 Symphony of Sentience, Cosmos & Life: In Memoriam, Anna-Teresa Tymieniecka, Olga Louchakova-Schwartz; 2 World Phenomenology Institute's Eco-Phenomenology, Daniela Verducci; 3 Metaphysics & EP Aiming at Harmony of Human Life with Cosmos, Francesco Totaro; 4 Ontopoiesis of Life as EP, Carmen Cozma; 5 EP: Philosophical Sources & Main Concepts, Maija Kule; 6 Logos of Life & Logos of Science. Metaphysical Advice, Gianfranco Bosio; 7 Insight into Foundations of EP, Massimo Marassi; 8 Questions About Idealism & Realism in Structure of Husserlian Phenomenology, Dario Sacchi; 9 Origin Paradox: How Could Life Emerge from Nonlife?, Ion Soteropoulos; 10 Fundamental Biological Activity of Universe, Attila Grandpierre; 11 Cosmic Harmony, Emergence of Life & Human Consciousness, Mamuka Dolidze; 12 Welt. At Origins of EP: Heidegger's Concept of 'World' in Work of Anna-Teresa Tymieniecka, Stefano Veluti; 13 Life & Human Life in System of World Coordinates: Extreme Dynamic Equilibriums, Nikolay Kozhevnikov, Vera Danilova; 14 EP Vision: Balancing Harmony of Earth, Debika Saha; 15 From Anna-Teresa Tymieniecka's EP to Paul Ricoeur's Hermeneutics. Cosmos, Chaos & Evil, Maria Avelina Cecilia Lafuente; 16 Ego: Cross Point of Divine Illumination & Social Reality, Konul Bunyadzade; 17 Phenomenology as Ecology: Movement from Ego- to Geo- & Eco-Thinking, Ella Buceniece; 18 'Song of the Earth': An EP, Kimiyo Murata-Soraci; 19 Cultural Sustainability: Lines of Reflection for Human Life in Harmony of Cosmos, Alessandra Lucaioli; 20 Geology of Movement. Earth & Dynamic of Phenomenalisation in Merleau-Ponty & Patocka, Renato Boccali; 21 On Two Versions of Phenomenological Transgression - Anna-Teresa Tymieniecka, Jean-Paul Sartre, Piotr Mroz; 22 Sowing 'A Quilt of Harmony': EP Reading of Ben Okri's 'Lines in Potentia' from Wild (2012), Rosemary Gray; 23 EP of Scientific Activity As Non-Routinized Routine: Stefan Banach's Cafe Method of Research, Bronislaw Bombala; 24 EP: Japanese Original Perspective in Thought of Nishida Kitaro, Valentina Carella; 25 'Negative Seeing': Robert Smithson, Earth Art, & EP of 'Mirror Displacements', Ming-Qian Ma; 26 Transcendental Philosophy of Krishnachandra: Indian Approach to Human Life, Koushik Joardar; 27 Henryk Skolimowski's Eco-Philosophy as Project of Living Philosophy, Anna Malecka, Katarzyna Stark; 28 Small Talk with Grape Vine: Presence & Sensuous Depth of Being, Lena Hopsch; 29 Auditory Phenomena & Human Life: Phenomenological Experience, Ineta Kivle; 30 Animal Being Means Desiring: Subjectivity, Singularity, Diversity in Post-Human Life, Roberto Marchesini; 31 Language That (In)Habits Us, Antonio Dominguez Rey; 32 Phenomenological Elucidations Phenomenological Language, Erkut Sezgin; 33 Phenomenology After Conceptual Art, Andrew Chesher; 34 Human Condition, Nature, Power & Creativity. Biopolitics, Massimo Mezzanzanica; 35 Concept of Life in Ludwig Binswanger's Phenomenological Psychopathology, Anna Piazza; 36 Meaningless Life: Clinical Phenomenology in Understanding 'Being in World' of Psychiatric Patients, Giulio Lo Bello; 37 Ecological Perspective on Helping Relationship, Antonio De Luca; 38 Experience of City: EP Perspective, Velga Vevere; 39 EP of Human Environment: Case of Intercultural Dialogue, Angela Ales Bello; 40 Digital Reason vs. Modern 'Metamorphosis of Man': Philosophical Anthropology of Jozef Baika & Anna-Teresa Tymieniecka, Jan Szmyd; 41 From Archeology of Happenings . Matter of Corporeality, Aleksandra Pawliszyn; 42 Multi-layered Time & Unity of Unfolding Logos of Life, Kamil Lacina; 43 Question of Placeness, Carla Danani; 44 Holographic Memory of Life Situation, Salahaddin Khalilov; 45 Chronotopic Content of Esoterism & Models of Thought, Sadaqat M. Aliyeva; 46 Phenomenology of Virtual Body: An Introduction, Roberto Diodato.…

paperback. Zustand: New. Language:Chinese. Binding:paperback.

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

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

Hardback. Zustand: New. 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.

Zustand: As New. Unread book in perfect condition.

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.…

Zustand: New.

Zustand: New.

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

Hardback. Zustand: New. 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. In English.