finelybook
Machine Learning Algorithms in Depth
by 作者: Vadim Smolyakov (Author)
Publisher Finelybook 出版社: Manning
Publication Date 出版日期: 2024-06-04
Language 语言: English
pages 页数: : 325 pages
ISBN-10 书号: 1633439216
ISBN-13 书号: 9781633439214
Book Description
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.
From the Back Cover
Machine Learning Algorithms in Depth dives deep into the 'how' and the 'why' of machine learning algorithms. For each category of an algorithm, you will go from math-first principles to hands-on implementation in Python. You will 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 will know how major algorithms work under the hood -- and be a better machine learning practitioner.
About the reader
For intermediate machine learning practitioners familiar with linear algebra, probability, and basic calculus.
About the Author
Machine Learning Algorithms in Depth MEAP V09
1. Copyright_2023_Manning_Publications
2. welcome
3. 1_Machine_Learning_Algorithms
4. 2_Markov_Chain_Monte_Carlo
5. 3_Variational_Inference
6. 4_Software_Implementation
7. 5_Classification_Algorithms
8. 6_Regression_Algorithms
9. 7_Selected_Supervised_Learning_Algorithms
10. 8_Fundamental_Unsupervised_Learning_Algorithms
11. 9_Selected_Unsupervised_Learning_Algorithms
12. 10_Fundamental_Deep_Learning_Algorithms
13. 11_Advanced_Deep_Learning_Algorithms
14. Appendix_A._Further_Reading_and_Resources
15. Appendix_B._Answers_to_Exercises
