rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Automated deep learning using neural network intelligence
    sub title: develop and design PyTorch and TensorFlow models using Python
  name:
    name part: Gridin, Ivan
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  origin info:
    place:
      place term: nyu
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations (chiefly color)
  abstract: 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 develo…
  table of contents: 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.
  note: Ivan Gridin.
  note: Includes index.
  subject:
    topic: Deep learning (Machine learning)
  subject:
    topic: Neural networks (Computer science)
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Deep learning (Machine learning)
  subject:
    topic: Neural networks (Computer science)
  subject:
    topic: Python (Computer program language)
  classification: Q325.73
  classification: 006.3/1
  related item:
    identifier: (OCoLC)1302579224
  identifier: 9781484281499
  identifier: 1484281497
  identifier: 9781484281482
  identifier: 1484281489
  identifier: 9781264286362 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 220625
    record change date: 20260129204401.5
    record identifier: in00004671690
    language of cataloging:
      language term: eng