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248276813
Model-Based Machine Learning
Item #:
248276813
PKR 42979
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This book tackles the challenge of connecting the abstract mathematics of machine learning to real-world problems through model-based approaches.
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- Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system.The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem.Features:Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems.Explains machine learning concepts as they arise in real-world case studies.Shows how to diagnose, understand and address problems with machine learning systems.Full source code available, allowing models and results to be reproduced and explored.Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.
| Item Weight | 1.5 lbs (680 grams) |
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Model-Based Machine Learning
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PKR 42979
This product is not Fulfilled by Ubuy and can take minimum 10 days in delivery. We might cancel the product from the order and refund you if any issue arise with the delivery of this product.
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Features & Benefits
- Focuses on connecting machine learning techniques to real-world problems.
- Introduces key ideas through practical case studies.
- Discusses the modelling assumptions in machine learning systems.
- Demonstrates step-by-step solutions using a model-based approach.
- Provides full source code for reproducibility and exploration.
- Includes in-depth sections for readers interested in mathematical details.
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