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Machine Learning Algorithms in Depth
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 Algorithm s in Depth you’ll explore practical implementations of dozens of ML 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’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.
In Machine Learning Algorithm s in Depth you’ll explore practical implementations of dozens of ML 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’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.
328 pages, Paperback
Published August 27, 2024
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Displaying 1 - 1 of 1 review
September 5, 2024
"Machine Learning Algorithms in Depth" is a must-read for anyone with a deep interest in the nuts and bolts of machine learning. Written by an author who transitioned from wireless communications to machine learning during his graduate studies, this book stands out for its unique approach—bridging mathematical derivation with hands-on implementation. The author's passion for algorithms shines through, particularly in how he developed a fascination for Bayesian inference and neural networks during his time at MIT's CSAIL.
What sets this book apart is its commitment to a "from-scratch" methodology. Many machine learning texts skim over the intricate details of algorithm development, but this book digs into the underlying principles, offering readers the chance to derive, code, and analyze algorithms at a fundamental level. The author’s "aha!" moment—realizing the gap in the literature for a hands-on approach—feels like a shared discovery with the reader.
The exploration of both probabilistic graphical models and deep learning, and their potential synergy, is an exciting angle that gives the book depth and forward-thinking relevance in an ever-evolving field. From newcomers wanting a solid technical foundation to seasoned professionals seeking fresh insights, this book offers an inspiring blend of theory and practical application, ultimately encouraging self-learning and innovation.
Machine Learning Algorithms in Depth is an excellent resource for anyone aiming to dig deeper into the world of machine learning algorithms and develop the intuition to innovate within the field.
What sets this book apart is its commitment to a "from-scratch" methodology. Many machine learning texts skim over the intricate details of algorithm development, but this book digs into the underlying principles, offering readers the chance to derive, code, and analyze algorithms at a fundamental level. The author’s "aha!" moment—realizing the gap in the literature for a hands-on approach—feels like a shared discovery with the reader.
The exploration of both probabilistic graphical models and deep learning, and their potential synergy, is an exciting angle that gives the book depth and forward-thinking relevance in an ever-evolving field. From newcomers wanting a solid technical foundation to seasoned professionals seeking fresh insights, this book offers an inspiring blend of theory and practical application, ultimately encouraging self-learning and innovation.
Machine Learning Algorithms in Depth is an excellent resource for anyone aiming to dig deeper into the world of machine learning algorithms and develop the intuition to innovate within the field.
Displaying 1 - 1 of 1 review