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Machine Learning Algorithms in Depth by Vadim Smolyakov (English) Hardcover Book
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N.º de artículo de eBay:396836172011
Características del artículo
- Estado
- ISBN-13
- 9781633439214
- Book Title
- Machine Learning Algorithms in Depth
- ISBN
- 9781633439214
Acerca de este producto
Product Identifiers
Publisher
Manning Publications Co. LLC
ISBN-10
1633439216
ISBN-13
9781633439214
eBay Product ID (ePID)
21059343037
Product Key Features
Number of Pages
328 Pages
Language
English
Publication Name
Machine Learning Algorithms in Depth
Subject
Programming / Algorithms, Programming Languages / Python
Publication Year
2024
Type
Textbook
Subject Area
Computers
Format
Hardcover
Dimensions
Item Height
0.6 in
Item Weight
21.4 Oz
Item Length
9.1 in
Item Width
7.1 in
Additional Product Features
LCCN
2024-409027
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.31
Synopsis
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 Simulation Image Denoising using Mean-Field Variational Inference EM algorithm for Hidden Markov Models Imbalanced Learning, Active Learning and Ensemble Learning Bayesian Optimisation for Hyperparameter Tuning Dirichlet Process K-Means for Clustering Applications Stock Clusters based on Inverse Covariance Estimation Energy Minimisation using Simulated Annealing Image Search based on ResNet Convolutional Neural Network Anomaly 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., 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 eBook 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, 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.
LC Classification Number
Q325.5.S6 2023
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grandeagleretail
98,2% de votos positivos•2,8 millones artículos vendidos
Registrado como vendedor profesional
Votos de vendedor (1.056.001)
- c***a (121)- Votos emitidos por el comprador.Mes pasadoCompra verificadaThe seller was very responsive and answered me on a timely matter. The product itself came in its packaging and was new, not used at all. The packaging was not beat up or anything, safely delivered to my mailbox. No mix ups and zero stress with delivery. The price for the product is completely understandable for the product. I really appreciate the seller and I am very happy to have purchased through this seller. Completely trustable!
- m***4 (1603)- Votos emitidos por el comprador.Mes pasadoCompra verificadaLeaving positive feedback because 1) item was packed well & arrived as described 2) seller did give partial refund when subsequent price dropped below org purchase price. 3) communication was quick However, there was a downside to this transaction -item listed as in-stock but ended up waiting nearly a month for them to get it from their distributer then ship it to me (bought June 29th, arrived around July 21). Auction said 12-15 days. Better clarity would have prevented lot of frustration
- w***i (875)- Votos emitidos por el comprador.Mes pasadoCompra verificadaAbsolutely Wonderful Seller!! Terrific Item As Described!!! Great Service and Communication!! Shipped In Waterproof Packaging!! I Received Item In About One Week!! Very Pleased With Seller! I Will Buy From This Seller Again!!