{"product_id":"automated-deep-learning-using-neural-network-intelligence-develop-and-design-pytorch-and-tensorflow-models-using-python-paperback","title":"Automated Deep Learning Using Neural Network Intelligence: Develop and Design Pytorch and Tensorflow Models Using Python - Paperback","description":"\u003cp\u003eby \u003cb\u003eIvan Gridin\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eOptimize, 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. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e 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. \u003cp\u003e\u003c\/p\u003e 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.\u003cbr\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eKnow the basic concepts of optimization tuners, search space, and trials\u003c\/li\u003e\n\u003cli\u003eApply different hyper-parameter optimization algorithms to develop effective neural networks\u003c\/li\u003e\n\u003cli\u003eConstruct new deep learning models from scratch\u003c\/li\u003e\n\u003cli\u003eExecute the automated Neural Architecture Search to create state-of-the-art deep learning models\u003c\/li\u003e\n\u003cli\u003eCompress the model to eliminate unnecessary deep learning layers\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cb\u003eWho This Book Is For \u003c\/b\u003e\u003cb\u003e\u003cbr\u003e\u003c\/b\u003eIntermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003eOptimize, 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.\u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eAfter 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\u003cbr\u003e\u003cul\u003e\n\u003cli\u003eKnow the basic concepts of optimization tuners, search space, and trials\u003c\/li\u003e\n\u003cli\u003eApply different hyper-parameter optimization algorithms to develop effective neural networks\u003c\/li\u003e\n\u003cli\u003eConstruct new deep learning models from scratch\u003c\/li\u003e\n\u003cli\u003eExecute the automated Neural Architecture Search to create state-of-the-art deep learning models\u003c\/li\u003e\n\u003cli\u003eCompress the model to eliminate unnecessary deep learning layers\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003cb\u003eIvan Gridin\u003c\/b\u003e 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.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 384\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.83 x 10 x 7 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 21, 2022\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42120184496263,"sku":"9781484281482","price":75.58,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0601\/2623\/2711\/files\/87d97e50ea8ecd5cd6758e53a40b1b68.webp?v=1732545714","url":"https:\/\/booksby.splitshops.com\/products\/automated-deep-learning-using-neural-network-intelligence-develop-and-design-pytorch-and-tensorflow-models-using-python-paperback","provider":"Books by splitShops","version":"1.0","type":"link"}