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231010 ||| eng |
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|a 9781800201132
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|a QA76.9.D43
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|a Madani, Ali
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|a Debugging machine learning models with Python
|b develop high-performance, low-bias, and explainable machine learning and deep learning models
|c Ali Madani ; foreword by Stephen MacKinnon
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| 260 |
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|a Birmingham, UK
|b Packt Publishing Ltd.
|c 2023
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| 300 |
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|a 1 online resource
|b illustrations
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|a Don't interpret your plots as you wish -- Bias and variance diagnosis -- Model validation strategy -- Error analysis -- Beyond performance -- Summary -- Questions -- References -- Chapter 5: Improving the Performance of Machine Learning Models -- Technical requirements -- Options for improving model performance -- Grid search -- Random search -- Bayesian search -- Successive halving -- Synthetic data generation -- Oversampling for imbalanced data -- Improving pre-training data processing -- Anomaly detection and outlier removal -- Benefitting from data of lower quality or relevance
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|a Best practices for high-quality Python programming -- Version control -- Debugging beyond Python -- Flaws in data used for modeling -- Data format and structure -- Data quantity and quality -- Data biases -- Model and prediction-centric debugging -- Underfitting and overfitting -- Inference in model testing and production -- Data or hyperparameters for changing landscapes -- Summary -- Questions -- References -- Chapter 2: Machine Learning Life Cycle -- Technical requirements -- Before we start modeling -- Data collection -- Data selection -- Data exploration -- Data wrangling -- Structuring
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|a System manipulation -- Secure and private machine learning techniques -- Transparency in machine learning modeling -- Accountable and open to inspection modeling -- Data and model governance -- Summary -- Questions -- References -- Part 2: Improving Machine Learning Models -- Chapter 4: Detecting Performance and Efficiency Issues in Machine Learning Models -- Technical requirements -- Performance and error assessment measures -- Classification -- Regression -- Clustering -- Visualization for performance assessment -- Summary metrics are not enough -- Visualizations could be misleading
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|a Enriching -- Data transformation -- Cleaning -- Modeling data preparation -- Feature selection and extraction -- Designing an evaluation and testing strategy -- Model training and evaluation -- Testing the code and the model -- Model deployment and monitoring -- Summary -- Questions -- References -- Chapter 3: Debugging toward Responsible AI -- Technical requirements -- Impartial modeling fairness in machine learning -- Data bias -- Algorithmic bias -- Security and privacy in machine learning -- Data privacy -- Data poisoning -- Adversarial attacks -- Output integrity attacks
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|a Cover -- Title Page -- Copyright -- Dedication -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1: Debugging for Machine Learning Modeling -- Chapter 1: Beyond Code Debugging -- Technical requirements -- Machine learning at a glance -- Types of machine learning modeling -- Supervised learning -- Unsupervised learning -- Self-supervised learning -- Semi-supervised learning -- Reinforcement learning -- Generative machine learning -- Debugging in software development -- Error messages in Python -- Debugging techniques -- Debuggers
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| 653 |
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|a Machine learning / Computer simulation
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|a Debugging in computer science / Computer programs / http://id.loc.gov/authorities/subjects/sh86002705
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|a Debugging in computer science / Computer programs / fast
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| 653 |
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|a Python (Computer program language) / fast
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| 653 |
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|a Python (Langage de programmation)
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| 653 |
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|a Apprentissage automatique / Simulation par ordinateur
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| 653 |
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|a Débogueurs
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 700 |
1 |
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|a MacKinnon, Stephen
|e writer of foreword
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Includes index
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| 015 |
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|a GBC3I5959
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| 776 |
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|z 9781800201132
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| 776 |
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|z 1800208588
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| 776 |
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|z 1800201133
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| 776 |
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|z 9781800208582
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9781800208582/?ar
|x Verlag
|3 Volltext
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|a 005.1/4
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|a 500
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|a Debugging 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
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