eBook
Debugging Machine Learning Models with Python : Develop High-performance, Low-bias, and Explainable Machine Learning and Deep Learning Models
| Τίτλος: | Debugging Machine Learning Models with Python : Develop High-performance, Low-bias, and Explainable Machine Learning and Deep Learning Models |
|---|---|
| Περιγραφή: | Master reproducible ML and DL models with Python and PyTorch to achieve high performance, explainability, and real-world successKey FeaturesLearn how to improve performance of your models and eliminate model biasesStrategically design your machine learning systems to minimize chances of failure in productionDiscover advanced techniques to solve real-world challengesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDebugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-performance models for industrial applications. Whether you're a data scientist, analyst, machine learning engineer, or Python developer, this book will empower you to design modular systems for data preparation, accurately train and test models, and seamlessly integrate them into larger technologies. By bridging the gap between theory and practice, you'll learn how to evaluate model performance, identify and address issues, and harness recent advancements in deep learning and generative modeling using PyTorch and scikit-learn. Your journey to developing high quality models in practice will also encompass causal and human-in-the-loop modeling and machine learning explainability. With hands-on examples and clear explanations, you'll develop the skills to deliver impactful solutions across domains such as healthcare, finance, and e-commerce.What you will learnEnhance data quality and eliminate data flawsEffectively assess and improve the performance of your modelsDevelop and optimize deep learning models with PyTorchMitigate biases to ensure fairnessUnderstand explainability techniques to improve model qualitiesUse test-driven modeling for data processing and modeling improvementExplore techniques to bring reliable models to productionDiscover the benefits of causal and human-in-the-loop modelingWho this book is forThis book is for data scientists, analysts, machine learning engineers, Python developers, and students looking to build reliable, high-performance, and explainable machine learning models for production across diverse industrial applications. Fundamental Python skills are all you need to dive into the concepts and practical examples covered. Whether you're new to machine learning or an experienced practitioner, this book offers a breadth of knowledge and practical insights to elevate your modeling skills. |
| Συγγραφείς: | Ali Madani |
| Resource Type: | eBook. |
| Θέματα: | Python (Computer program language), Debugging in computer science--Computer programs, Machine learning--Computer simulation |
| Categories: | COMPUTERS / Artificial Intelligence / General, COMPUTERS / Computer Science, COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition |
| Βάση Δεδομένων: | eBook Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edsebk DbLabel: eBook Index An: 3669686 RelevancyScore: 969 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 968.509704589844 |
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| Items | – Name: Title Label: Title Group: Ti Data: Debugging Machine Learning Models with Python : Develop High-performance, Low-bias, and Explainable Machine Learning and Deep Learning Models – Name: Abstract Label: Description Group: Ab Data: Master reproducible ML and DL models with Python and PyTorch to achieve high performance, explainability, and real-world successKey FeaturesLearn how to improve performance of your models and eliminate model biasesStrategically design your machine learning systems to minimize chances of failure in productionDiscover advanced techniques to solve real-world challengesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDebugging Machine Learning Models with Python is a comprehensive guide that navigates you through the entire spectrum of mastering machine learning, from foundational concepts to advanced techniques. It goes beyond the basics to arm you with the expertise essential for building reliable, high-performance models for industrial applications. Whether you're a data scientist, analyst, machine learning engineer, or Python developer, this book will empower you to design modular systems for data preparation, accurately train and test models, and seamlessly integrate them into larger technologies. By bridging the gap between theory and practice, you'll learn how to evaluate model performance, identify and address issues, and harness recent advancements in deep learning and generative modeling using PyTorch and scikit-learn. Your journey to developing high quality models in practice will also encompass causal and human-in-the-loop modeling and machine learning explainability. With hands-on examples and clear explanations, you'll develop the skills to deliver impactful solutions across domains such as healthcare, finance, and e-commerce.What you will learnEnhance data quality and eliminate data flawsEffectively assess and improve the performance of your modelsDevelop and optimize deep learning models with PyTorchMitigate biases to ensure fairnessUnderstand explainability techniques to improve model qualitiesUse test-driven modeling for data processing and modeling improvementExplore techniques to bring reliable models to productionDiscover the benefits of causal and human-in-the-loop modelingWho this book is forThis book is for data scientists, analysts, machine learning engineers, Python developers, and students looking to build reliable, high-performance, and explainable machine learning models for production across diverse industrial applications. Fundamental Python skills are all you need to dive into the concepts and practical examples covered. Whether you're new to machine learning or an experienced practitioner, this book offers a breadth of knowledge and practical insights to elevate your modeling skills. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ali+Madani%22">Ali Madani</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Python+%28Computer+program+language%29%22">Python (Computer program language)</searchLink><br /><searchLink fieldCode="DE" term="%22Debugging+in+computer+science--Computer+programs%22">Debugging in computer science--Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning--Computer+simulation%22">Machine learning--Computer simulation</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Artificial+Intelligence+%2F+General%22">COMPUTERS / Artificial Intelligence / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Computer+Science%22">COMPUTERS / Computer Science</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Artificial+Intelligence+%2F+Computer+Vision+%26+Pattern+Recognition%22">COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=3669686 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 005.14 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Python (Computer program language) Type: general – SubjectFull: Debugging in computer science--Computer programs Type: general – SubjectFull: Machine learning--Computer simulation Type: general Titles: – TitleFull: Debugging Machine Learning Models with Python : Develop High-performance, Low-bias, and Explainable Machine Learning and Deep Learning Models Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ali Madani – PersonEntity: Name: NameFull: Ali Madani IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 – D: 26 M: 09 Type: profile Y: 2023 Identifiers: – Type: isbn-print Value: 9781800208582 – Type: isbn-electronic Value: 9781800201132 Titles: – TitleFull: Debugging Machine Learning Models with Python : Develop High-performance, Low-bias, and Explainable Machine Learning and Deep Learning Models Type: main |
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