Debugging machine learning models with Python : develop high-performance, low-bias, and explainable machine learning and deep learning models /

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-perfor...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Madani, Ali (Autor)
Otros Autores: MacKinnon, Stephen (writer of foreword.)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing Ltd., 2023.
Temas:
Machine learning > Computer simulation.
Debugging in computer science > Computer programs.
Python (Computer program language)
Apprentissage automatique > Simulation par ordinateur.
Débogueurs.
Python (Langage de programmation)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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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