Interpretable Machine Learning with Python. Build explainable, fair, and robust high-performance models with hands-on, real-world examples - Second Edition

Autorzy:: Serg Masís, Aleksander Molak, Denis Rothman

Interpretable Machine Learning with Python. Build explainable, fair, and robust high-performance models with hands-on, real-world examples - Second Edition Serg Masís, Aleksander Molak, Denis Rothman - okladka książki

Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 606
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Do przechowalni

Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.

Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.

In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.

By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.

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O autorach książki

Aleksander Molak jest niezależnym badaczem i konsultantem w dziedzinie uczenia maszynowego. Współpracował z licznymi firmami w Europie, USA i Izraelu, gdzie uczestniczył w tworzeniu wielkoskalowych systemów uczenia maszynowego. Jest też współzałożycielem firmy Lespire.io, dostawcy szkoleń z zakresu sztucznej inteligencji dla zespołów korporacyjnych.

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive natural language processing (NLP) chatbots applied as an automated language teacher for Moët et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an advanced planning and scheduling (APS) solution used worldwide.

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Szczegóły książki

Tytuł oryginału:: Interpretable Machine Learning with Python. Build explainable, fair, and robust high-performance models with hands-on, real-world examples - Second Edition
ISBN Ebooka:: 978-18-032-4362-7, 9781803243627
Data wydania ebooka :: 2023-10-31 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
Język publikacji:: 1
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Informatyka » Biznes IT » Big data » Uczenie maszynowe

Spis treści książki

1. Interpretation, Interpretability and Explainability; and why does it all matter?
2. Key Concepts of Interpretability
3. Interpretation Challenges
4. Global Model-agnostic Interpretation Methods
5. Local Model-agnostic Interpretation Methods
6. Anchors and Counterfactual Explanations
7. Visualizing Convolutional Neural Networks
8. Interpreting NLP Transformers
9. Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
10. Feature Selection and Engineering for Interpretability
11. Bias Mitigation and Causal Inference Methods
12. Monotonic Constraints and Model Tuning for Interpretability
13. Adversarial Robustness
14. What's Next for Machine Learning Interpretability?

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