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

    (ebook) (audiobook) (audiobook) Język publikacji: angielski
    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 - okładka ebooka

    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 - okładka ebooka

    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 - okładka audiobooka MP3

    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 - okładka audiobooks CD

    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    606
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    ePub

    Ebook

    139,00 zł

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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 ebooka

    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.

    Serg Masís, Aleksander Molak, Denis Rothman - pozostałe książki

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