Automated deep learning using de gridin ivan (24 resultados)
- Tapa blanda
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 44,16
Envío por EUR 2,32Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
- Tapa blanda
Librería: Lakeside Books, Benton Harbor, MI, Estados Unidos de AmericaLakeside Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 42,95
Envío por EUR 3,50Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books.
- Tapa blanda
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de AmericaBargainBookStores
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 47,74
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 5 disponibles
Paperback or Softback. Condición: New. Automated Deep Learning Using Neural Network Intelligence: Develop and Design Pytorch and Tensorflow Models Using Python. Book.
- Tapa blanda
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de AmericaGreatBookPrices
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 49,66
Envío por EUR 2,32Se envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: As New. Unread book in perfect condition.
- Tapa blanda
Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 52,44
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
- Tapa blanda
- Primera edición
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 62,82
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning mod…el development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level.What You Will LearnKnow the basic concepts of optimization tuners, search space, and trialsApply different hyper-parameter optimization algorithms to develop effective neural networksConstruct new deep learning models from scratchExecute the automated Neural Architecture Search to create state-of-the-art deep learning modelsCompress the model to eliminate unnecessary deep learning layersWho This Book Is For Intermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development Intermediate-Advanced user level Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Tapa blanda
- Primera edición
Librería: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 61,82
Envío por EUR 10,50Se envía de Irlanda a Estados Unidos de AmericaCantidad disponible: 15 disponibles
Condición: New. 2022. 1st ed. Paperback. . . . . .
- Tapa blanda
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Como Nuevo
EUR 57,26
Envío por EUR 17,38Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: As New. Unread book in perfect condition.
- Tapa blanda
Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 59,55
Envío por EUR 17,38Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
- Tapa blanda
Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 64,44
Envío por EUR 13,88Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. In.
- Tapa blanda
Librería: Chiron Media, Wallingford, Reino UnidoChiron Media
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 61,08
Envío por EUR 17,95Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 10 disponibles
PF. Condición: New.
- Tapa blanda
Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 71,99
Envío por EUR 14,48Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Paperback. Condición: Brand New. 401 pages. 10.00x7.00x0.83 inches. In Stock.
- Tapa blanda
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de AmericaKennys Bookstore
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 77,90
Envío por EUR 9,22Se envía dentro de Estados Unidos de AmericaCantidad disponible: 15 disponibles
Condición: New. 2022. 1st ed. Paperback. . . . . . Books ship from the US and Ireland.
- Tapa blanda
Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 99,56
Envío por EUR 3,50Se envía dentro de Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. 1st ed. edition NO-PA16APR2015-KAP.
- Tapa blanda
- Primera edición
Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 95,07
Envío por EUR 32,48Se envía de Australia a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning mod…el development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level.What You Will LearnKnow the basic concepts of optimization tuners, search space, and trialsApply different hyper-parameter optimization algorithms to develop effective neural networksConstruct new deep learning models from scratchExecute the automated Neural Architecture Search to create state-of-the-art deep learning modelsCompress the model to eliminate unnecessary deep learning layersWho This Book Is For Intermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development Intermediate-Advanced user level Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Tapa blanda
Librería: preigu, Osnabrück, Alemaniapreigu
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 63,80
Envío por EUR 70,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 5 disponibles
Taschenbuch. Condición: Neu. Automated Deep Learning Using Neural Network Intelligence | Develop and Design Pytorch and Tensorflow Models Using Python | Ivan Gridin | Taschenbuch | xvii | Englisch | 2022 | Apress | EAN 9781484281482 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Pl…atz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
- Tapa blanda
Librería: Buchpark, Trebbin, AlemaniaBuchpark
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado
EUR 37,32
Envío por EUR 105,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 7 disponibles
Condición: Hervorragend. Zustand: Hervorragend | Seiten: 384 | Sprache: Englisch | Produktart: Bücher | Intermediate-Advanced user level.
- Tapa blanda
Librería: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, AlemaniaBUCHSERVICE / ANTIQUARIAT Lars Lutzer
Contactar con el vendedorVendedor de 5 estrellasCondición: Usado - Bueno
EUR 189,90
Envío por EUR 39,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Softcover. Condición: gut. 2022. Automated Deep Learning Using Neural Network Intelligence In deutscher Sprache. pages.
- Tapa blanda
- Impresión bajo demanda
Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 65,63
Envío por EUR 14,48Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: Brand New. 401 pages. 10.00x7.00x0.83 inches. In Stock. This item is printed on demand.
- Tapa blanda
- Impresión bajo demanda
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 69,54
Envío por EUR 23,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches…and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level.What You Will LearnKnow the basic concepts of optimization tuners, search space, and trialsApply different hyper-parameter optimization algorithms to develop effective neural networksConstruct new deep learning models from scratchExecute the automated Neural Architecture Search to create state-of-the-art deep learning modelsCompress the model to eliminate unnecessary deep learning layersWho This Book Is ForIntermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development 384 pp. Englisch.
- Tapa blanda
- Impresión bajo demanda
Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 101,36
Envío por EUR 7,53Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. Print on Demand.
- Tapa blanda
- Impresión bajo demanda
Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 99,27
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 4 disponibles
Condición: New. PRINT ON DEMAND.
- Tapa blanda
- Impresión bajo demanda
Librería: moluna, Greven, Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 62,02
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learn…ing approaches and provides technique.
- Tapa blanda
- Impresión bajo demanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 76,28
Envío por EUR 63,79Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and p…rovides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level.What You Will LearnKnow the basic concepts of optimization tuners, search space, and trialsApply different hyper-parameter optimization algorithms to develop effective neural networksConstruct new deep learning models from scratchExecute the automated Neural Architecture Search to create state-of-the-art deep learning modelsCompress the model to eliminate unnecessary deep learning layersWho This Book Is ForIntermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development.
