Machine Learning Algorithms in Depth
Seiten
2024
Manning Publications (Verlag)
978-1-63343-921-4 (ISBN)
Manning Publications (Verlag)
978-1-63343-921-4 (ISBN)
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.
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.
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.
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.
| Erscheinungsdatum | 22.11.2023 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 180 x 230 mm |
| Gewicht | 607 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Informatik ► Theorie / Studium ► Algorithmen | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-63343-921-6 / 1633439216 |
| ISBN-13 | 978-1-63343-921-4 / 9781633439214 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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