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Machine Learning and Optimization Models for Optimization in Cloud, Gupta Punit, Goyal Mayank Kumar, Chakraborty Sudeshna


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Автор: Gupta Punit, Goyal Mayank Kumar, Chakraborty Sudeshna
Название:  Machine Learning and Optimization Models for Optimization in Cloud
ISBN: 9781032028200
Издательство: Taylor&Francis
Классификация:

ISBN-10: 1032028203
Обложка/Формат: Hardcover
Страницы: 204
Вес: 0.49 кг.
Дата издания: 28.02.2022
Серия: Chapman & hall/distributed computing and intelligent data analytics series
Язык: English
Иллюстрации: 20 tables, black and white; 92 line drawings, black and white; 1 halftones, black and white; 93 illustrations, black and white
Размер: 23.39 x 15.60 x 1.42 cm
Читательская аудитория: Postgraduate, research & scholarly
Ссылка на Издательство: Link
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Поставляется из: Европейский союз
Описание: Cloud computing has been a new trend in problem-solving and providing reliable computing platform for big and high computational tasks. This technique is used for business industries like banking, trading and many e-commerce businesses to accommodate high request rate, high availability for all time without stopping system and system failure.


Bandit Algorithms

Автор: Tor Lattimore, Csaba Szepesvari
Название: Bandit Algorithms
ISBN: 1108486827 ISBN-13(EAN): 9781108486828
Издательство: Cambridge Academ
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Цена: 6970.00 р.
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Описание: Decision-making in the face of uncertainty is a challenge in machine learning, and the multi-armed bandit model is a common framework to address it. This comprehensive introduction is an excellent reference for established researchers and a resource for graduate students interested in exploring stochastic, adversarial and Bayesian frameworks.

Beyond the Worst-Case Analysis of Algorithms

Автор: Tim Roughgarden
Название: Beyond the Worst-Case Analysis of Algorithms
ISBN: 1108494315 ISBN-13(EAN): 9781108494311
Издательство: Cambridge Academ
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Цена: 9187.00 р.
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Описание: Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case analysis. The book introduces exciting new methods for assessing algorithm performance.

Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient

Автор: Agrawal Tanay
Название: Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient
ISBN: 1484265785 ISBN-13(EAN): 9781484265789
Издательство: Springer
Цена: 7685.00 р.
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Описание:

  • ​Chapter 1: Hyperparameters
Chapter Goal: To introduce what hyperparameters are, how they can affect themodel training. Also gives an intuition of how hyperparameter affects general machinelearning algorithms, and what value should we choose as per the training dataset.Sub - Topics1. Introduction to hyperparameters.2. Why do we need to tune hyperparameters3. Specific algorithms and their hyperparameters4. Cheatsheet for deciding Hyperparameter of some specific Algorithms.
Chapter 2: Brute Force Hyperparameter TuningChapter Goal: To understand the commonly used classical hyperparameter tuningmethods and implement them from scratch, as well as use the Scikit-Learn library to do so.Sub - Topics: 1. Hyperparameter tuning2. Exhaustive hyperparameter tuning methods3. Grid search4. Random search5. Evaluation of models while tuning hyperparameters.
Chapter 3: Distributed Hyperparameter OptimizationChapter Goal: To handle bigger datasets and a large number of hyperparameterwith continuous search spaces using distributed algorithms and distributedhyperparameter optimization methods, using Dask Library.Sub - Topics: 1. Why we need distributed tuning2. Dask dataframes3. IncrementalSearchCV
Chapter 4: Sequential Model-Based Global Optimization and Its HierarchicalMethodsChapter Goal: A detailed theoretical chapter about SMBO Methods, which usesBayesian techniques to optimize hyperparameter. They learn from their previous iterationunlike Grid Search or Random Search.Sub - Topics: 1. Sequential Model-Based Global Optimization2. Gaussian process approach3. Tree-structured Parzen Estimator(TPE)
Chapter 5: Using HyperOptChapter Goal: A Chapter focusing on a library hyperopt that implements thealgorithm TPE discussed in the last chapter. Goal to use the TPE algorithm to optimizehyperparameter and make the reader aware of how it is better than other methods.MongoDB will be used to parallelize the evaluations. Discuss Hyperopt Scikit-Learn and Hyperas with examples.1. Defining an objective function.2. Creating search space.3. Running HyperOpt.4. Using MongoDB Trials to make parallel evaluations.5. HyperOpt SkLearn6. Hyperas
Chapter 6: Hyperparameter Generating Condition Generative Adversarial NeuralNetworks(HG-cGANs) and So Forth.Chapter Goal: It is based on a hypothesis of how, based on certain properties of dataset, one can train neural networks on metadata and generate hyperparameters for new datasets. It also summarizes how these newer methods of Hyperparameter Tuning can help AI to develop further.Sub - Topics: 1. Generating Metadata2. Training HG-cGANs3. AI and hyperparameter tuning
Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

Автор: Masood Adnan
Название: Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
ISBN: 1800567685 ISBN-13(EAN): 9781800567689
Издательство: Неизвестно
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Цена: 9010.00 р.
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Описание:

Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies


Key Features:

  • Get up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choice
  • Eliminate mundane tasks in data engineering and reduce human errors in machine learning models
  • Find out how you can make machine learning accessible for all users to promote decentralized processes


Book Description:

Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort.


This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you'll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle.


By the end of this machine learning book, you'll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks.


What You Will Learn:

  • Explore AutoML fundamentals, underlying methods, and techniques
  • Assess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenario
  • Find out the difference between cloud and operations support systems (OSS)
  • Implement AutoML in enterprise cloud to deploy ML models and pipelines
  • Build explainable AutoML pipelines with transparency
  • Understand automated feature engineering and time series forecasting
  • Automate data science modeling tasks to implement ML solutions easily and focus on more complex problems


Who this book is for:

Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.

Machine Learning, Optimization, and Big Data

Автор: Panos Pardalos; Mario Pavone; Giovanni Maria Farin
Название: Machine Learning, Optimization, and Big Data
ISBN: 3319279254 ISBN-13(EAN): 9783319279251
Издательство: Springer
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Цена: 7826.00 р.
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Описание: This bookconstitutes revised selected papers from the First International Workshop onMachine Learning, Optimization, and Big Data, MOD 2015, held in Taormina, Sicily,Italy, in July 2015. The 32papers presented in this volume were carefully reviewed and selected from 73submissions.

Introduction to Applied Linear Algebra

Автор: Boyd Stephen
Название: Introduction to Applied Linear Algebra
ISBN: 1316518965 ISBN-13(EAN): 9781316518960
Издательство: Cambridge Academ
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Цена: 6811.00 р.
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Описание: A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance.

Algorithms for Optimization

Автор: Kochenderfer Mykel J., Wheeler Tim A.
Название: Algorithms for Optimization
ISBN: 0262039427 ISBN-13(EAN): 9780262039420
Издательство: MIT Press
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Цена: 14390.00 р.
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Описание: A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems.

This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language.

Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.

Logistics Management and Optimization through Hybrid Artificial Intelligence Systems

Автор: Carlos Alberto Ochoa Ortiz Zezzatti, Camelia Chira, Arturo Hernandez, Miguel Basurto
Название: Logistics Management and Optimization through Hybrid Artificial Intelligence Systems
ISBN: 146660297X ISBN-13(EAN): 9781466602977
Издательство: Mare Nostrum (Eurospan)
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Цена: 28413.00 р.
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Описание: Logistics Management and Optimization through Hybrid Artificial Intelligence Systems offers the latest research within the field of HAIS, surveying the broad topics and collecting case studies, future directions, and cutting edge analyses. Using biologically inspired algorithms such as ant colony optimization and particle swarm optimization, this text includes solutions and heuristics for practitioners and academics alike, offering a vital resource for staying abreast in this ever-burgeoning field.

Optimization for machine learning

Название: Optimization for machine learning
ISBN: 0262537761 ISBN-13(EAN): 9780262537766
Издательство: MIT Press
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Цена: 13794.00 р.
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Описание: An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities.

The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields.
Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

Linear algebra and optimization with applications to machine learning - volume i: linear algebra for computer vision, robotics, and machine learning

Автор: Gallier, Jean H (univ Of Pennsylvania, Usa) Quaintance, Jocelyn (univ Of Pennsylvania, Usa)
Название: Linear algebra and optimization with applications to machine learning - volume i: linear algebra for computer vision, robotics, and machine learning
ISBN: 9811207712 ISBN-13(EAN): 9789811207716
Издательство: World Scientific Publishing
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Цена: 14256.00 р.
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Описание:

This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.

Machine Learning, Optimization, and Data Science

Автор: Giuseppe Nicosia; Panos Pardalos; Giovanni Giuffri
Название: Machine Learning, Optimization, and Data Science
ISBN: 3030137082 ISBN-13(EAN): 9783030137083
Издательство: Springer
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Цена: 11459.00 р.
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Описание: This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.The 46 full papers presented were carefully reviewed and selected from 126 submissions.

Optimization in Machine Learning and Applications

Автор: Anand J. Kulkarni; Suresh Chandra Satapathy
Название: Optimization in Machine Learning and Applications
ISBN: 981150993X ISBN-13(EAN): 9789811509933
Издательство: Springer
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Цена: 16769.00 р.
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Описание: This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making.


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