Artículos relacionados a Shallow Learning vs. Deep Learning: A Practical Guide...

Shallow Learning vs. Deep Learning: A Practical Guide for Machine Learning Solutions (The Springer Series in Applied Machine Learning) - Tapa dura

 
9783031694981: Shallow Learning vs. Deep Learning: A Practical Guide for Machine Learning Solutions (The Springer Series in Applied Machine Learning)

Sinopsis

This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends.

In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del autor

Omer Faruk Ertugrul, was born in Batman, Turkey in 1978. He received the B.S. degree from the Hacettepe University, Department of Electrical and Electronics Engineering in 2001, M.S. and Ph.D. degrees in Electrical and Electronics Engineering in 2010, and 2015, respectively. His research interests include machine learning and signal processing. He is in 100,000 top-scientists list in the world, %2 top-scientist list in the world, in Turkey Top 10.000 Scientists, and in AD Scientific Index - 2022 Turkey Top 10.000 Scientists, in 2019, 2020, 2021, and 2022 respectively. He is currently associate editor in NC&A (SCI-E indexed-Q1) in Middle East excluding Iran. He is also co-founder/co-owner and CTO in INSENSE, ABRH and SOFTSENSE. 

Josep M. Guerrero received his B.Sc. (1997), M.Sc. (2000), and Ph.D. (2003) in engineering from the Technical University of Catalonia, Barcelona. He is currently pursuing an M.Sc. in Psychobiology and Cognitive Neuroscience. Since 2011, he has been a Full Professor at AAU Energy, Aalborg University, Denmark, leading the Microgrid Research Program. In 2019, he founded the Center for Research on Microgrids (CROM). His research covers microgrids, IoT, cybersecurity, maritime and space microgrids, and smart medical systems. He is an Associate Editor for IEEE TRANSACTIONS and has over 900 papers with 117,000 citations. Recognized as a Highly Cited Researcher (2014-2022), he received the IEEE Bimal Bose Award (2021) and IEEE PES Douglas M. Staszesky Award (2022).

Musa Yilmaz received his Associate Professor certificate in Electrical-Electronics and Communication Engineering. He works at the University of California, Riverside, and Batman University. He received his M.Sc. degree from Marmara University, Istanbul, Turkey, in 2004, and his Ph.D. degree from the same institution in 2013. From 2015 to 2016, Dr. Yilmaz was a visiting scholar at the Smart Grid Research Center (SMERC) at the University of California, Los Angeles (UCLA). His primary research interests include smart grid technologies, renewable energy, machine learning, and signal processing. Dr. Yilmaz is a partner of the medical company Biosys LLC. He has served as Editor-in-Chief of the Balkan Journal of Electrical and Computer Engineering (BAJECE) and the European Journal of Technique (EJT). Additionally, he is the owner of INESEG, a publishing organization. Dr. Yilmaz has authored over 50 research articles, several book chapters, and frequently delivers invited keynote lectures at international conferences. He has also led his research team as the Principal Investigator in several European projects. He is an IEEE Senior Member.

De la contraportada

This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends.

In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields.

"Sobre este título" puede pertenecer a otra edición de este libro.

Comprar nuevo

Ver este artículo

EUR 11,00 gastos de envío desde Alemania a España

Destinos, gastos y plazos de envío

Resultados de la búsqueda para Shallow Learning vs. Deep Learning: A Practical Guide...

Imagen del vendedor

Ömer Faruk Ertugrul
ISBN 10: 3031694988 ISBN 13: 9783031694981
Nuevo Tapa dura
Impresión bajo demanda

Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends.In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields. 275 pp. Englisch. Nº de ref. del artículo: 9783031694981

Contactar al vendedor

Comprar nuevo

EUR 149,79
Convertir moneda
Gastos de envío: EUR 11,00
De Alemania a España
Destinos, gastos y plazos de envío

Cantidad disponible: 2 disponibles

Añadir al carrito

Imagen del vendedor

Ömer Faruk Ertu¿rul
ISBN 10: 3031694988 ISBN 13: 9783031694981
Nuevo Tapa dura

Librería: AHA-BUCH GmbH, Einbeck, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends.In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields. Nº de ref. del artículo: 9783031694981

Contactar al vendedor

Comprar nuevo

EUR 149,79
Convertir moneda
Gastos de envío: EUR 11,99
De Alemania a España
Destinos, gastos y plazos de envío

Cantidad disponible: 1 disponibles

Añadir al carrito

Imagen de archivo

Publicado por Springer, 2024
ISBN 10: 3031694988 ISBN 13: 9783031694981
Nuevo Tapa dura

Librería: Ria Christie Collections, Uxbridge, Reino Unido

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Condición: New. In. Nº de ref. del artículo: ria9783031694981_new

Contactar al vendedor

Comprar nuevo

EUR 157,39
Convertir moneda
Gastos de envío: EUR 4,72
De Reino Unido a España
Destinos, gastos y plazos de envío

Cantidad disponible: Más de 20 disponibles

Añadir al carrito

Imagen de archivo

Publicado por Springer, 2024
ISBN 10: 3031694988 ISBN 13: 9783031694981
Nuevo Tapa dura

Librería: California Books, Miami, FL, Estados Unidos de America

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Condición: New. Nº de ref. del artículo: I-9783031694981

Contactar al vendedor

Comprar nuevo

EUR 171,26
Convertir moneda
Gastos de envío: EUR 7,00
De Estados Unidos de America a España
Destinos, gastos y plazos de envío

Cantidad disponible: Más de 20 disponibles

Añadir al carrito

Imagen del vendedor

Ömer Faruk Ertu¿rul
ISBN 10: 3031694988 ISBN 13: 9783031694981
Nuevo Tapa dura

Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Buch. Condición: Neu. Neuware -This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends.In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 288 pp. Englisch. Nº de ref. del artículo: 9783031694981

Contactar al vendedor

Comprar nuevo

EUR 149,79
Convertir moneda
Gastos de envío: EUR 35,00
De Alemania a España
Destinos, gastos y plazos de envío

Cantidad disponible: 2 disponibles

Añadir al carrito

Imagen del vendedor

Publicado por Springer Verlag GmbH, 2024
ISBN 10: 3031694988 ISBN 13: 9783031694981
Nuevo Tapa dura

Librería: moluna, Greven, Alemania

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Condición: New. Nº de ref. del artículo: 1756047207

Contactar al vendedor

Comprar nuevo

EUR 176,64
Convertir moneda
Gastos de envío: EUR 19,49
De Alemania a España
Destinos, gastos y plazos de envío

Cantidad disponible: Más de 20 disponibles

Añadir al carrito

Imagen de archivo

OEmer Faruk Ertugrul
ISBN 10: 3031694988 ISBN 13: 9783031694981
Nuevo Tapa dura

Librería: CitiRetail, Stevenage, Reino Unido

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Hardcover. Condición: new. Hardcover. This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends.In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9783031694981

Contactar al vendedor

Comprar nuevo

EUR 165,54
Convertir moneda
Gastos de envío: EUR 35,59
De Reino Unido a España
Destinos, gastos y plazos de envío

Cantidad disponible: 1 disponibles

Añadir al carrito

Imagen de archivo

OEmer Faruk Ertugrul
ISBN 10: 3031694988 ISBN 13: 9783031694981
Nuevo Tapa dura

Librería: AussieBookSeller, Truganina, VIC, Australia

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Hardcover. Condición: new. Hardcover. This book explores the ongoing debate between shallow and deep learning in the field of machine learning. It provides a comprehensive survey of machine learning methods, from shallow learning to deep learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a machine learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends.In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing, speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and deep learning in these areas, the book provides a framework for thoughtful selection and application of machine learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different machine learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9783031694981

Contactar al vendedor

Comprar nuevo

EUR 186,03
Convertir moneda
Gastos de envío: EUR 32,38
De Australia a España
Destinos, gastos y plazos de envío

Cantidad disponible: 1 disponibles

Añadir al carrito

Imagen de archivo

Publicado por Springer, 2024
ISBN 10: 3031694988 ISBN 13: 9783031694981
Nuevo Tapa dura

Librería: Books Puddle, New York, NY, Estados Unidos de America

Calificación del vendedor: 4 de 5 estrellas Valoración 4 estrellas, Más información sobre las valoraciones de los vendedores

Condición: New. Nº de ref. del artículo: 26402316425

Contactar al vendedor

Comprar nuevo

EUR 209,34
Convertir moneda
Gastos de envío: EUR 10,06
De Estados Unidos de America a España
Destinos, gastos y plazos de envío

Cantidad disponible: 4 disponibles

Añadir al carrito

Imagen de archivo

Publicado por Springer, 2024
ISBN 10: 3031694988 ISBN 13: 9783031694981
Nuevo Tapa dura
Impresión bajo demanda

Librería: Majestic Books, Hounslow, Reino Unido

Calificación del vendedor: 5 de 5 estrellas Valoración 5 estrellas, Más información sobre las valoraciones de los vendedores

Condición: New. Print on Demand. Nº de ref. del artículo: 395141974

Contactar al vendedor

Comprar nuevo

EUR 215,85
Convertir moneda
Gastos de envío: EUR 10,50
De Reino Unido a España
Destinos, gastos y plazos de envío

Cantidad disponible: 4 disponibles

Añadir al carrito

Existen otras 3 copia(s) de este libro

Ver todos los resultados de su búsqueda