Friedland, Martin L (SIGNED)Biblio.com
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2002007861Downsview Toronto Ontario: University of Toronto Press 2002. 1st Edition . Hardcover. Fine. 4to - over 9¾ - 12" tall. Black boards FINE with gilt lettering on spine in black slipcase NEAR FINE condition with gilt UofT emblem / lettering. xiii 764 pages with illustrations archival photos bibliography & index. SIGNED & dated May 14 2002 by Friedland on title page. Due to weight extra shipping will be required. Please contact us by email to determine the accurate postal rate. Uncommon im the slipcase edition <br/> <br/> University of Toronto Press hardcover
2008Q-1423446801Hal Leonard 2008-01-01. Paperback. New. In shrink wrap. Looks like an interesting title! Hal Leonard paperback
2016x-1349949248Palgrave Macmillan 2016. Hardcover. New. 316 pages. 8.50x6.25x1.00 inches. Palgrave Macmillan hardcover
1998Q-0793579945Hal Leonard 1998-11-01. Paperback. New. In shrink wrap. Looks like an interesting title! Hal Leonard paperback
A9780521764513Hardback. New. This innovative textbook presents an experiential holistic approach to multimedia computing along with practical algorithms. hardcover
2010Q-1439905231Temple University Press 2010-10-01. Hardcover. New. In shrink wrap. Looks like an interesting title! Temple University Press hardcover
20171-1531000819Carolina Academic Press 2017. Paperback. New. 4th edition. 616 pages. 10.00x7.00x1.25 inches. Carolina Academic Press paperback
SONG131906566XW. H. Freeman 2015-12-14. Second. paperback. Used: Good. 8.54x0.70x10.76. Buy with confidence. Excellent Customer Service & Return policy. W. H. Freeman paperback
1531016626New. Brand new and still unused unknown
20112512190908001W. H. Freeman 2011-02-25. Paperback. Good. Nice looking book has minor edge wear on corners. has used label on rear cover. W. H. Freeman paperback
1997Q-0793579953HAL LEONARD CORPORATION 1997-10-01. Paperback. New. New. In shrink wrap. Looks like an interesting title! HAL LEONARD CORPORATION paperback
1996Q-0521440467Cambridge University Press 1996-03-29. Hardcover. New. New. In shrink wrap. Looks like an interesting title! Cambridge University Press hardcover
1856910707CGHamburg:, B. S. Berendsohn, 1856. Stahlstich 12 x 19 cm, Blattgröße 24 x 33 cm.
1990G-968-381AldineTransaction 1990. Good. Former library book. No dust jacket. Different cover. Edition 1990. Ammareal gives back up to 15% of this item's net price to charity organizations. AldineTransaction unknown
3031394798.Gpaperback. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book. paperback
23233084-nnew. unknown
650508037University of Toronto Press pp. 504 . Papeback. New. University of Toronto Press unknown
2015x-1474228860Bloomsbury USA Academic 2015. Paperback. New. reprint edition. 250 pages. 9.25x6.25x0.75 inches. Bloomsbury USA Academic paperback
1996Q-0814726593NYU Press 1996-08-01. Paperback. New. In shrink wrap. Looks like an interesting title! NYU Press paperback
2013UJune2019-314279431-1119West Academic Publishing 2013-04-02. Paperback. Good. Textbook May Have Highlights Notes and/or Underlining BOOK ONLY-NO ACCESS CODE NO CD Ships with Emailed Tracking West Academic Publishing paperback
20284399-nnew. unknown
20284399like new. unknown
2010DADAX1464100756Brand: W. H. Freeman 2012-01-04. First Edition. paperback. New. 8.53x0.59x10.89. Buy with confidence. Excellent Customer Service & Return policy. Brand: W. H. Freeman 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
