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 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...

Descripción completa

Detalles Bibliográficos
Otros Autores: Madanipour, Ali, author (author), MacKinnon, Stephen, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt Publishing [2023]
Edición:1st ed
Materias:
Machine learning > Computer simulation.
Debugging in computer science > Computer programs.
Python (Computer program language)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009767135506719
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
  • Regularization to improve model generalizability
  • Summary
  • Questions
  • References
  • Chapter 6: Interpretability and Explainability in Machine Learning Modeling
  • Technical requirements
  • Interpretable versus black-box machine learning
  • Interpretable machine learning models
  • Explainability for complex models
  • Explainability methods in machine learning
  • Local explainability techniques
  • Global explanation
  • Practicing machine learning explainability in Python
  • Explanations in SHAP
  • Explanations using LIME
  • Counterfactual generation using Diverse Counterfactual Explanations (DiCE)
  • Reviewing why having explainability is not enough
  • Summary
  • Questions
  • References
  • Chapter 7: Decreasing Bias and Achieving Fairness
  • Technical requirements.
  • Fairness in machine learning modeling
  • Proxies for sensitive variables
  • Sources of bias
  • Biases introduced in data generation and collection
  • Bias in model training and testing
  • Bias in production
  • Using explainability techniques
  • Fairness assessment and improvement in Python
  • Summary
  • Questions
  • References
  • Part 3: Low-Bug Machine Learning Development and Deployment
  • Chapter 8: Controlling Risks Using Test-Driven Development
  • Technical requirements
  • Test-driven development for machine learning modeling
  • Unit testing
  • Machine learning differential testing
  • Tracking machine learning experiments
  • Summary
  • Questions
  • References
  • Chapter 9: Testing and Debugging for Production
  • Technical requirements
  • Infrastructure testing
  • Infrastructure as Code tools
  • Infrastructure testing tools
  • Infrastructure testing using Pytest
  • Integration testing of machine learning pipelines
  • Integration testing using pytest
  • Monitoring and validating live performance
  • Model assertion
  • Summary
  • Questions
  • References
  • Chapter 10: Versioning and Reproducible Machine Learning Modeling
  • Technical requirements
  • Reproducibility in machine learning
  • Data versioning
  • Model versioning
  • Summary
  • Questions
  • References
  • Chapter 11: Avoiding and Detecting Data and Concept Drifts
  • Technical requirements
  • Avoiding drifts in your models
  • Avoiding data drift
  • Addressing concept drift
  • Detecting drifts
  • Practicing with alibi_detect for drift detection
  • Practicing with evidently for drift detection
  • Summary
  • Questions
  • References
  • Part 4: Deep Learning Modeling
  • Chapter 12: Going Beyond ML Debugging with Deep Learning
  • Technical requirements
  • Introduction to artificial neural networks
  • Optimization algorithms
  • Frameworks for neural network modeling.
  • PyTorch for deep learning modeling
  • Summary
  • Questions
  • References
  • Chapter 13: Advanced Deep Learning Techniques
  • Technical requirements
  • Types of neural networks
  • Categorization based on data type
  • Convolutional neural networks for image shape data
  • Performance assessment
  • CNN modeling using PyTorch
  • Image data transformation and augmentation for CNNs
  • Using pre-trained models
  • Transformers for language modeling
  • Tokenization
  • Language embedding
  • Language modeling using pre-trained models
  • Modeling graphs using deep neural networks
  • Graph neural networks
  • GNNs with PyTorch Geometric
  • Summary
  • Questions
  • References
  • Chapter 14: Introduction to Recent Advancements in Machine Learning
  • Technical requirements
  • Generative modeling
  • Generative deep learning techniques
  • Prompt engineering for text-based generative models
  • Generative modeling using PyTorch
  • Reinforcement learning
  • Reinforcement learning with human feedback (RLHF)
  • Self-supervised learning (SSL)
  • Self-supervised learning with PyTorch
  • Summary
  • Questions
  • References
  • Part 5: Advanced Topics in Model Debugging
  • Chapter 15: Correlation versus Causality
  • Technical requirements
  • Correlation as part of machine learning models
  • Causal modeling to reduce risks and improve performance
  • Assessing causation in machine learning models
  • Causal inference
  • Causal modeling using Python
  • Using dowhy for causal effect estimation
  • Using bnlearn for causal inference through Bayesian networks
  • Summary
  • Questions
  • References
  • Chapter 16: Security and Privacy in Machine Learning
  • Technical requirements
  • Encryption techniques and their use in machine learning
  • Implementing AES encryption in Python
  • Homomorphic encryption
  • Differential privacy
  • Federated learning
  • Summary
  • Questions
  • References.
  • Chapter 17: Human-in-the-Loop Machine Learning
  • Humans in the machine learning life cycle
  • Expert feedback collection
  • Human-in-the-loop modeling
  • Summary
  • Questions
  • References
  • Assessments
  • Chapter 1 - Beyond Code Debugging
  • Chapter 2 - Machine Learning Life Cycle
  • Chapter 3 - Debugging toward Responsible AI
  • Chapter 4 - Detecting Performance and Efficiency Issues in Machine Learning Models
  • Chapter 5 - Improving the Performance of Machine Learning Models
  • Chapter 6 - Interpretability and Explainability in Machine Learning Modeling
  • Chapter 7 - Decreasing Bias and Achieving Fairness
  • Chapter 8 - Controlling Risks Using Test-Driven Development
  • Chapter 9 - Testing and Debugging for Production
  • Chapter 10 - Versioning and Reproducible Machine Learning Modeling
  • Chapter 11 - Avoiding and Detecting Data and Concept Drifts
  • Chapter 12 - Going Beyond ML Debugging with Deep Learning
  • Chapter 13 - Advanced Deep Learning Techniques
  • Chapter 14 - Introduction to Recent Advancements in Machine Learning
  • Chapter 15 - Correlation versus Causality
  • Chapter 16 - Security and Privacy in Machine Learning
  • Chapter 17 - Human-in-the-Loop Machine Learning
  • Index
  • About Packt
  • Other Books You May Enjoy.

Ejemplares similares