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Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
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EUR 72,10
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Idioma: Inglés
Publicado por Manning Publications, New York, 2024
ISBN 10: 1633439216 ISBN 13: 9781633439214
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 83,39
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Añadir al carritoHardcover. Condición: new. Hardcover. Develop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: Monte Carlo Stock Price SimulationImage Denoising using Mean-Field Variational InferenceEM algorithm for Hidden Markov ModelsImbalanced Learning, Active Learning and Ensemble LearningBayesian Optimisation for Hyperparameter TuningDirichlet Process K-Means for Clustering ApplicationsStock Clusters based on Inverse Covariance EstimationEnergy Minimisation using Simulated AnnealingImage Search based on ResNet Convolutional Neural NetworkAnomaly Detection in Time-Series using Variational Autoencoders Each algorithm is fully explored with both math and practical implementations so you can see how they work and put into action. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML engineer. This vital knowledge lets you modify algorithms to your specific needs, understand the trade-offs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
EUR 65,52
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EUR 76,36
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Idioma: Inglés
Publicado por Manning Publications 2023-08-29, 2023
ISBN 10: 1633439216 ISBN 13: 9781633439214
Librería: Chiron Media, Wallingford, Reino Unido
EUR 67,55
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Original o primera edición
EUR 75,38
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Añadir al carritoCondición: New. 2024. 1st Edition. paperback. . . . . .
Idioma: Inglés
Publicado por Manning Publications, US, 2024
ISBN 10: 1633439216 ISBN 13: 9781633439214
Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de America
EUR 89,69
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Añadir al carritoHardback. Condición: New. Develop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: Monte Carlo Stock Price SimulationImage Denoising using Mean-Field Variational InferenceEM algorithm for Hidden Markov ModelsImbalanced Learning, Active Learning and Ensemble LearningBayesian Optimisation for Hyperparameter TuningDirichlet Process K-Means for Clustering ApplicationsStock Clusters based on Inverse Covariance EstimationEnergy Minimisation using Simulated AnnealingImage Search based on ResNet Convolutional Neural NetworkAnomaly Detection in Time-Series using Variational Autoencoders Each algorithm is fully explored with both math and practical implementations so you can see how they work and put into action. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML engineer. This vital knowledge lets you modify algorithms to your specific needs, understand the trade-offs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs.
EUR 75,43
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Librería: Romtrade Corp., STERLING HEIGHTS, MI, Estados Unidos de America
EUR 90,68
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EUR 94,10
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Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 90,87
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EUR 80,41
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Añadir al carritoPaperback. Condición: Brand New. 325 pages. 9.25x7.37x0.81 inches. In Stock.
EUR 96,29
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EUR 89,28
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Añadir al carritopaperback. Condición: New. Special order direct from the distributor.
Librería: Kennys Bookstore, Olney, MD, Estados Unidos de America
EUR 96,93
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Añadir al carritoCondición: New. 2024. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.
EUR 91,74
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Añadir al carritoPaperback. Condición: Brand New. 325 pages. 9.25x7.37x0.81 inches. In Stock.
Idioma: Inglés
Publicado por Manning Publications, US, 2024
ISBN 10: 1633439216 ISBN 13: 9781633439214
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 108,18
Cantidad disponible: 1 disponibles
Añadir al carritoHardback. Condición: New. Develop a mathematical intuition around machine learning algorithms to improve model performance and effectively troubleshoot complex ML problems. For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus. Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you will learn the fundamentals of Bayesian inference and deep learning. You will also explore the core data structures and algorithmic paradigms for machine learning. You will explore practical implementations of dozens of ML algorithms, including: Monte Carlo Stock Price SimulationImage Denoising using Mean-Field Variational InferenceEM algorithm for Hidden Markov ModelsImbalanced Learning, Active Learning and Ensemble LearningBayesian Optimisation for Hyperparameter TuningDirichlet Process K-Means for Clustering ApplicationsStock Clusters based on Inverse Covariance EstimationEnergy Minimisation using Simulated AnnealingImage Search based on ResNet Convolutional Neural NetworkAnomaly Detection in Time-Series using Variational Autoencoders Each algorithm is fully explored with both math and practical implementations so you can see how they work and put into action. About the technology Fully understanding how machine learning algorithms function is essential for any serious ML engineer. This vital knowledge lets you modify algorithms to your specific needs, understand the trade-offs when picking an algorithm for a project, and better interpret and explain your results to your stakeholders. This unique guide will take you from relying on one-size-fits-all ML libraries to developing your own algorithms to solve your business needs.
EUR 63,18
Cantidad disponible: 6 disponibles
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Idioma: Inglés
Publicado por LAP LAMBERT Academic Publishing, 2020
ISBN 10: 6202679328 ISBN 13: 9786202679329
Librería: Books Puddle, New York, NY, Estados Unidos de America
EUR 112,61
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