Friedland, GeraldBiblio.com
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2023x-3031394763Springer-Nature New York Inc 2023. Hardcover. New. 289 pages. 9.25x6.10x9.21 inches. Springer-Nature New York Inc hardcover
3031394798.Gpaperback. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book. paperback
B9783031394768Hardback. New. <p>This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field.</p> <p>Stemming 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 the discipline's robustness and credibility.</p> <p>While bridging the gap between machine learning and disciplines such as physics information theory and computer engineering this textbook maintains an accessible and comprehensive style making complex topics digestible for a broad readership. <i>Information-Driven Machine Learning</i> explores the synergistic harmony among these disciplines to enhance our understanding of data science modeling. Instead of solely focusing on the "how" this text provides answers to the "why" questions that permeate the field shedding light on the underlying principles of machine learning processes and their practical implications. By advocating for systematic methodologies grounded in fundamental principles this book challenges industry practices that have often evolved from ideologic or profit-driven motivations. It addresses a range of topics including deep learning data drift and MLOps using fundamental principles such as entropy capacity and high dimensionality.</p> <p>Ideal for both academia and industry professionals this textbook serves as a valuable tool for those seeking to deepen their understanding of data science as an engineering discipline. Its thought-provoking content stimulates intellectual curiosity and caters to readers who desire more than just code or ready-made formulas. The text invites readers to explore beyond conventional viewpoints offering an alternative perspective that promotes a big-picture view for integrating theory with practice. Suitable for upper undergraduate or graduate-level courses this book can also benefit practicing engineers and scientists in various disciplines by enhancing their understanding of modeling and improving data measurement effectively.</p><br /><p></p> hardcover
ria9783031394768_inpHardcover. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field. Stemming from a UC Berkeley seminar on experimental design for machine learni hardcover
46830729-nnew. unknown
46830729like new. unknown
6399324591Springer pp. 336 . Hardback. New. Springer hardcover
2024Adhya-9783031394768SPRINGER 2024. Hardcover. New. SPRINGER hardcover
2024Adhya-9783031394768SPRINGER 2024. Hardcover. New. SPRINGER hardcover
2023Atlantic-9783031394768Springer 2023. 1. Hardcover. New. Springer hardcover
2023Atlantic-9783031394768Springer 2023. 1. Hardcover. New. Springer hardcover
2004AME_9781841843735MartiDunitz 2004. 1ST. Hardcover. New/New. MartiDunitz hardcover
363924849X.Gpaperback. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book. paperback
2002Q-0300091907Yale University Press 2002-01-15. Paperback. New. New. In shrink wrap. Looks like an interesting title! Yale University Press paperback
6928079Hal Leonard Corporation pp. 144 Complete Edition . Other. New. Hal Leonard Corporation unknown
2005Q-0634089358Hal Leonard 2005-11-01. Sheet music. New. New. In shrink wrap. Looks like an interesting title! Hal Leonard unknown
1996Q-079356381XHAL LEONARD CORPORATION 1996-04-01. Paperback. New. New. In shrink wrap. Looks like an interesting title! HAL LEONARD CORPORATION paperback
1996Q-0793563763Hal Leonard 1996-04-01. Paperback. New. New. In shrink wrap. Looks like an interesting title! Hal Leonard paperback
1996Q-079356378XHal Leonard Corporation 1996-04-01. Paperback. New. In shrink wrap. Looks like an interesting title! Hal Leonard Corporation paperback
1996Q-0793563828HAL LEONARD CORPORATION 1996-04-01. Plastic Comb. New. New. In shrink wrap. Looks like an interesting title! HAL LEONARD CORPORATION unknown
1996Q-0793563836Hal Leonard 1996-05-01. Spiral-bound. New. New. In shrink wrap. Looks like an interesting title! Hal Leonard unknown
1996Q-0793563771HAL LEONARD CORPORATION 1996-04-01. Paperback. New. In shrink wrap. Looks like an interesting title! HAL LEONARD CORPORATION paperback
1996Q-0793563798Hal Leonard 1996-04-01. Paperback. New. In shrink wrap. Looks like an interesting title! Hal Leonard paperback
1996DADAX0793563828Hal Leonard 1996-04-01. paperback. New. 9.00x0.35x12.00. Buy with confidence. Excellent Customer Service & Return policy. Hal Leonard paperback
GOR004536517Paperback. Very Good. paperback
