Smolyakov (90 results)

Condition: New.

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Hardcover. Condition: New. A NEW COPY but with shelving/packing damage.; 100% Satisfaction Guaranteed! Ships same or next business day.

Condition: New.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Hardback. Condition: New.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New. In English.

Condition: Very Good. 1953. Pamphlet. In original wrappers, 18pp. Clean copy showing some age and light shelf wear. Remains a very good copy. . . . .

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: Very Good. 1953. Pamphlet. In original wrappers, 18pp. Clean copy showing some age and light shelf wear. Remains a very good copy. . . . . Books ship from the US and Ireland.

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread copy in mint condition.

Condition: New.

Condition: New.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Hard Cover. Condition: Very Good -. No Jacket. First Edition. 250 x 170mm. pp. 298. English text. Detailed volume on hot-wire anemometry and related transducers in relation to the measurement of turbulent fluctuations. Bound in original red boards. Library marks to endpages and base of spine, otherwise clean and tight. Binding strong. No underlining. Ex-Library. …

Condition: New.

Condition: new.

Condition: Poor. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In poor condition, suitable as a reading copy. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:3540121447.…

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Hardcover. Condition: 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. …

Condition: New.

Paperback. Condition: New.

Condition: New. 2024. 1st Edition. paperback. . . . . .

Hardback. Condition: 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.…

Condition: New. In English.

Condition: New.

Hardback. Condition: New. New copy - Usually dispatched within 4 working days.