Debugging Machine Learning Models with Python: Develop high-performance, low-bia

Description: Debugging Machine Learning Models with Python by Ali Madani, Stephen MacKinnon 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 youre 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, youll 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, youll 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 youre new to machine learning or an experienced practitioner, this book offers a breadth of knowledge and practical insights to elevate your modeling skills. FORMAT Paperback CONDITION Brand New Author Biography Ali Madani worked as the director of machine learning at Cyclica Inc leading AI technology development front of Cyclica for drug discovery before acquisition of Cyclica by Recursion Pharmaceuticals. Ali completed his PhD at University of Toronto focusing on machine learning modeling in cancer setting and attained a Master of Mathematics from the University of Waterloo. As a believer in industry-oriented education and pro-democratization of knowledge, Ali has actively educated students and professionals through international workshops and courses on basic and advanced high-quality machine learning modeling. When not immersed in machine learning modeling and teaching, Ali enjoys exercising, cooking and traveling with his partner. Table of Contents Table of ContentsBeyond Code DebuggingMachine Learning Life CycleDebugging toward Responsible AIDetecting Performance and Efficiency Issues in Machine Learning ModelsImproving the Performance of Machine Learning ModelsInterpretability and Explainability in Machine Learning ModelingDecreasing Bias and Achieving FairnessControlling Risks Using Test-Driven DevelopmentTesting and Debugging for ProductionVersioning and Reproducible Machine Learning ModelingAvoiding and Detecting Data and Concept DriftsGoing Beyond ML Debugging with Deep LearningAdvanced Deep Learning TechniquesIntroduction to Recent Advancements in Machine LearningCorrelation versus CausalitySecurity and Privacy in Machine LearningHuman-in-the-Loop Machine Learning Details ISBN1800208588 Author Stephen MacKinnon Publisher Packt Publishing Limited Year 2023 ISBN-13 9781800208582 Format Paperback Publication Date 2023-09-15 Imprint Packt Publishing Limited Subtitle Develop high-performance, low-bias, and explainable machine learning and deep learning models Place of Publication Birmingham Country of Publication United Kingdom Audience General UK Release Date 2023-09-15 Pages 344 DEWEY 005.1/4 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:159410382;

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