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Automated deep learning using neural...

Gridin, Ivan.

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Automated deep learning using neural network intelligence = develop and design pytorch and tensorflow models using python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Automated deep learning using neural network intelligence/ by Ivan Gridin.
Reminder of title:	develop and design pytorch and tensorflow models using python /
Author:	Gridin, Ivan.
Published:	Berkeley, CA :Apress : : 2022.,
Description:	xvii, 384 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction to Neural Network Intelligence -- Chapter 2:Hyperparameter Optimization -- Chapter 3: Hyperparameter Optimization Under Shell -- 4. Multi-Trial Neural Architecture Search -- Chapter 5: One-Shot Neural Architecture Search -- Chapter 6: Model Pruning -- Chapter 7: NNI Recipes.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-1-4842-8149-9
ISBN:	9781484281499

Automated deep learning using neural network intelligence = develop and design pytorch and tensorflow models using python /
Gridin, Ivan.

Automated deep learning using neural network intelligencedevelop and design pytorch and tensorflow models using python /[electronic resource] :by Ivan Gridin. - Berkeley, CA :Apress :2022. - xvii, 384 p. :ill. (chiefly color), digital ;24 cm.

Chapter 1: Introduction to Neural Network Intelligence -- Chapter 2:Hyperparameter Optimization -- Chapter 3: Hyperparameter Optimization Under Shell -- 4. Multi-Trial Neural Architecture Search -- Chapter 5: One-Shot Neural Architecture Search -- Chapter 6: Model Pruning -- Chapter 7: NNI Recipes.

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.

ISBN: 9781484281499

Standard No.: 10.1007/978-1-4842-8149-9doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .G75 2022

Dewey Class. No.: 006.31

Automated deep learning using neural network intelligence = develop and design pytorch and tensorflow models using python /
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520 $a 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.
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