Machine Learning Algorithms in Depth

Vadim Smolyakov

ISBN 10: 1633439216 / ISBN 13: 9781633439214

Published by Manning Publications Aug 2024, 2024

About this Item

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.

Seller Inventory # 9781633439214

Bibliographic Details

• Title: Machine Learning Algorithms in Depth
• Publisher: Manning Publications Aug 2024
• Publication Date: 2024
• Language: English
• ISBN 10: 1633439216
• ISBN 13: 9781633439214
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 232W x 185H x 17D millimeters 232 millimeters width by 185 millimeters height by 17 millimeters depth
• Weight: 566 grams

About this title

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.

About the Author

Vadim Smolyakov is a data scientist in 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.