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 Free Download Herbert Lieberman, "Pharmaceutical Dosage Forms: Disperse Systems" English | 2019 | pages: 553 | ISBN: 036740060X | PDF | 14,4 mb This third volume of the second edition offers information on specialized products such as emulsions, liposomes, polymers and polymeric pharmaceutical excipients. It explains the requirements for conducting clinical research and obtaining marketing approval for new drug products
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 Free Download Peter D. Walzer, Robert M. Genta, "Parasitic Infections in the Compromised Host (Infectious Disease and Therapy)" English | 1988 | pages: 552 | ISBN: 0824779436 | PDF | 68,6 mb The first in a new series created to acknowledge the explosion of knowledge in fields related to infectious disesases and clinical microbiology. Thirteen contributions focus on organisms which are of major medical importance in this country or which have contributed to an understanding of pathology.  Free Download PHP programmieren, 4. Auflage Deutsch | 2024 | ISBN: 9781098195533 | 544 Pages | EPUB | 4 MB Warum ist PHP die am häufigsten verwendete Programmiersprache im Internet? In dieser aktualisierten Ausgabe lernst du alles, was du wissen musst, um effektive Webanwendungen mit den neuesten Funktionen von PHP 7.4 zu erstellen. Du beginnst mit dem großen Ganzen und tauchst dann in die Syntax der Sprache, Programmiertechniken und andere Details ein, indem du Beispiele verwendest, die sowohl die korrekte Verwendung als auch gängige Redewendungen veranschaulichen. Wenn du bereits über HTML-Kenntnisse verfügst, geben dir die Autoren Kevin Tatroe und Peter MacIntyre viele Stiltipps und praktische Programmiertipps auf klare und übersichtliche Weise, damit du ein erstklassiger PHP-Programmierer wirst. ![Outsmart Your Brain Why Learning is Hard and How You Can Make It Easy [EPUB]](https://i124.fastpic.org/big/2024/0929/5e/5766ab9410d72db958179cbf763c7f5e.webp) Free Download Outsmart Your Brain: Why Learning is Hard and How You Can Make It Easy by Daniel T. Willingham Ph.D. English | January 24, 2023 | ISBN: 1982167173, 1788167767 | True EPUB | 336 pages | 4.3 MB In this revolutionary, comprehensive, and accessible guide on how the brain learns, discover how to study more efficiently and effectively, shrug away exam stress, and most of all, enjoy learning.  Free Download Paula Yurkanis Bruice, "Organic Chemistry, 8th edition" English | 2016 | ISBN: 9353948452, 0134581067, 013404228X | PDF | pages: 1340 | 70.2 mb Paula Bruice's presentation in Organic Chemistry, Eighth Edition provides mixed-science majors with the conceptual foundations, chemical logic, and problem-solving skills they need to reason their way to solutions for diverse problems in synthetic organic chemistry, biochemistry, and medicine. The Eighth Edition builds a strong framework for thinking about organic chemistry by unifying principles of reactivity that students will apply throughout the course, discouraging memorization. With more applications than any other textbook, Dr. Bruice consistently relates structure and reactivity to what occurs in our own cells and reinforces the fundamental reason for all chemical reactions-electrophiles react with nucleophiles. New streamlined coverage of substitution and elimination, updated problem-solving strategies, synthesis skill-building applications and tutorials guide students throughout fundamental and complex content in both the first and second semesters of the course.  Free Download Lori A. Dalton, Edward R. Dougherty, "Optimal Bayesian Classification" English | 2020 | pages: 360 | ISBN: 1510630694 | PDF | 4,2 mb The most basic problem of engineering is the design of optimal operators. Design takes different forms depending on the random process constituting the scientific model and the operator class of interest. For classification, the random process is a feature-label distribution, and a Bayes classifier minimizes classification error. Rarely do we know the feature-label distribution or have sufficient data to estimate it. To best use available knowledge and data, this book takes a Bayesian approach to modeling the feature-label distribution and designs an optimal classifier relative to a posterior distribution governing an uncertainty class of feature-label distributions. The origins of this approach lie in estimating classifier error when there are insufficient data to hold out test data, in which case an optimal error estimate can be obtained relative to the uncertainty class. A natural next step is to forgo classical ad hoc classifier design and find an optimal classifier relative to the posterior distribution over the uncertainty class this being an optimal Bayesian classifier. |
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