Machine Learning Algorithms in Depth

Vadim Smolyakov

ISBN 10: 1633439216 / ISBN 13: 9781633439214

Editore: Manning Publications Aug 2024, 2024

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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.

Codice articolo 9781633439214

Dati bibliografici

• Titolo: Machine Learning Algorithms in Depth
• Editore: Manning Publications Aug 2024
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 1633439216
• ISBN 13: 9781633439214
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 232 L x 185 A x 17 P (millimetri) 232 millimetri di larghezza per 185 millimetri di altezza per 17 millimetri di profondità
• Peso: 566 grammi

Descrizione articolo

Riassunto

Develop a mathematical intuition for how machine learning algorithms work so you can improve model performance and effectively troubleshoot complex ML problems.

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 probability-based 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, Kindle, and ePub formats from Manning Publications.

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 tradeoffs 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.

About the book

Machine Learning Algorithms in Depth dives deep into the how and the why of machine learning algorithms. For each category of algorithm, you’ll go from math-first principles to a hands-on implementation in Python. You’ll explore dozens of examples from across all the fields of machine learning, including finance, computer vision, NLP, and more. Each example is accompanied by worked-out derivations and details, as well as insightful code samples and graphics. By the time you’re done reading, you’ll know how major algorithms work under the hood—and be a better machine learning practitioner for it.

About the reader

For intermediate 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. He is a former PhD student in AI at MIT CSAIL with research interests in Bayesian inference and deep learning. Prior to joining Microsoft, Vadim developed machine learning solutions in the e-commerce space.

Informazioni sull?autore

Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. He is a former PhD student in AI at MIT CSAIL with research interests in Bayesian inference and deep learning. Prior to joining Microsoft, Vadim developed machine learning solutions in the e-commerce space.