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 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 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.

N� de ref. del art�culo 9781484281482

Contactar al vendedor

Denunciar este art�culo

Detalles bibliogr�ficos

T�tulo: Automated Deep Learning Using Neural Network Intelligence (Paperback)
Editor: APress, Berkley
Fecha de publicaci�n: 2022
Idioma: Ingl�s
ISBN 10: 1484281489
ISBN 13: 9781484281482
Encuadernaci�n: Paperback
Edici�n: 1� Edici�n
Estado: new

Acerca de

Sinopsis

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 Learn

Know the basic concepts of optimization tuners, search space, and trials
Apply different hyper-parameter optimization algorithms to develop effective neural networks
Construct new deep learning models from scratch
Execute the automated Neural Architecture Search to create state-of-the-art deep learning models
Compress the model to eliminate unnecessary deep learning layers

Who This Book Is For

Intermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development

Acerca del autor

Ivan Gridin is a machine learning expert from Moscow who has worked on distributive high-load systems and implemented different machine learning approaches in practice. One of the primary areas of his research is the design and analysis of predictive time series models. Ivan has fundamental math skills in probability theory, random process theory, time series analysis, machine learning, deep learning, and optimization. He has published books on genetic algorithms and time series analysis.

"Sobre este t�tulo" puede pertenecer a otra edici�n de este libro.

Descripci�n de la librer�a

Ver la p�gina web de la librer�a

Informaci�n empresarial del vendedor

The Nile Group Pty Ltd
42 Apex Drive, Truganina, VIC, 3029, Australia

Condiciones de venta y env�o

Condiciones de venta

We guarantee the condition of every book as it's described on the Abebooks web sites. If you're dissatisfied with your purchase (Incorrect Book/Not as Described/Damaged) or if the order hasn't arrived, you're eligible for a refund within 30 days of the estimated delivery date. If you've changed your mind about a book that you've ordered, please use the Ask bookseller a question link to contact us and we'll respond within 2 business days.

Derecho a cancelaci�n

Condiciones de env�o

Please note that titles are dispatched from our UK and NZ warehouse. Delivery times specified in shipping terms. Orders ship within 2 business days. Delivery to your door then takes 8-15 days.

Tarifas de env�o de Australia a Estados Unidos de America

Tarifas de env�o de Australia a Estados Unidos de America
Cantidad del pedido	De 25 a 45 d�as h�biles	De 8 a 14 d�as h�biles
Primer art�culo	EUR 31.59	EUR 37.57

Los plazos de entrega los establecen los vendedores y var�an seg�n el transportista y la ubicaci�n. Los pedidos que pasan por la aduana pueden sufrir retrasos y los compradores son responsables de los aranceles o tarifas asociadas. Los vendedores pueden ponerse en contacto con usted en relaci�n con cargos adicionales para cubrir cualquier aumento en los costes de env�o de los art�culos.