Information-Driven Machine Learning by Gerald Friedland - 9783031394799
£42.50
Information-Driven Machine LearningData Science as an Engineering Discipline\nAuthor(s): Gerald Friedland\nFormat: Paperback\nPublisher: Springer International Publishing AG, Switzerland\nImprint: Springer International Publishing AG\nISBN-13: 9783031394799, 978-3031394799\nSynopsis\nThis groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field.\n\nStemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overcome the 'black box' approach of machine learning by reducing conjectures such as magic numbers (hyper-parameters) or model-type bias. Information-based machine learning enables data quality measurements, a priori task complexity estimations, and reproducible design of data science experiments. The benefits include significant size reduction, increased explainability, and enhanced resilience of models, all contributing to advancing .
Information-Driven Machine Learning by Gerald Friedland - 9783031394799
Information-Driven Machine LearningData Science as an Engineering Discipline\nAuthor(s): Gerald Friedland\nFormat: Paperback\nPublisher: Springer International Publishing AG, Switzerland\nImprint: Springer International Publishing AG\nISBN-13: 9783031394799, 978-3031394799\nSynopsis\nThis groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field.\n\nStemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overcome the 'black box' approach of machine learning by reducing conjectures such as magic numbers (hyper-parameters) or model-type bias. Information-based machine learning enables data quality measurements, a priori task complexity estimations, and reproducible design of data science experiments. The benefits include significant size reduction, increased explainability, and enhanced resilience of models, all contributing to advancing .
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