Paperback. Zustand: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes Königreich
EUR 60,54
Anzahl: 5 verfügbar
In den WarenkorbHRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
EUR 76,36
Anzahl: 1 verfügbar
In den WarenkorbZustand: New.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 75,43
Anzahl: 6 verfügbar
In den WarenkorbZustand: New. In.
Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
EUR 80,41
Anzahl: 2 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 325 pages. 9.25x7.37x0.81 inches. In Stock.
Zustand: New. 2024. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.
EUR 91,74
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 325 pages. 9.25x7.37x0.81 inches. In Stock.
EUR 63,18
Anzahl: 6 verfügbar
In den WarenkorbZustand: NEW.
EUR 102,31
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 325 pages. 9.25x7.37x0.81 inches. In Stock.
Sprache: Spanisch
Verlag: Ediciones Nuestro Conocimiento, 2020
ISBN 10: 6202849584 ISBN 13: 9786202849586
Anbieter: moluna, Greven, Deutschland
EUR 40,73
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Sprache: Englisch
Verlag: Manning Publications Aug 2024, 2024
ISBN 10: 1633439216 ISBN 13: 9781633439214
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Neuware - Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance.Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you'll explore practical implementations of dozens of ML algorithms including: Monte Carlo Stock Price Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimization for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimization using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly Detection in Time-Series using Variational Autoencoders 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 probabilistic algorithms, you'll learn the fundamentals of Bayesian inference and deep learning. You'll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they're put into action. Purchase of the print book includes a free Elektronisches Buch in PDF and ePub formats from Manning Publications. About the technology Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods. About the book Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You'll especially appreciate author Vadim Smolyakov's clear interpretations of Bayesian algorithms for Monte Carlo and Markov models. What's inside Monte Carlo stock price simulation EM algorithm for hidden Markov models Imbalanced learning, active learning, and ensemble learning Bayesian optimization for hyperparameter tuning Anomaly detection in time-series About the reader For machine learning practitioners familiar with linear algebra, probability, and basic calculus. About the author Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. Table of Contents PART 1 1 Machine learning algorithms 2 Markov chain Monte Carlo 3 Variational inference 4 Software implementation PART 2 5 Classification algorithms 6 Regression algorithms 7 Selected supervised learning algorithms PART 3 8 Fundamental unsupervised learning algorithms 9 Selected unsupervised learning algorithms PART 4 10 Fundamental deep learning algorithms 11 Advanced deep learning algorithms.
EUR 40,73
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Sprache: Niederländisch
Verlag: Uitgeverij Onze Kennis, 2020
ISBN 10: 6202849614 ISBN 13: 9786202849616
Anbieter: moluna, Greven, Deutschland
EUR 40,73
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Sprache: Portugiesisch
Verlag: Edicoes Nosso Conhecimento, 2020
ISBN 10: 6202849630 ISBN 13: 9786202849630
Anbieter: moluna, Greven, Deutschland
EUR 40,73
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
EUR 40,73
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Verlag: Wydawnictwo Nasza Wiedza, 2020
ISBN 10: 6202849622 ISBN 13: 9786202849623
Anbieter: moluna, Greven, Deutschland
EUR 40,73
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.