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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
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