00987nam--2200337---450 99000138033020331620200428180909.0000138033USA01000138033(ALEPH)000138033USA0100013803320040129d1967----km-y0itay0103----baitaIT||||||||001yyRimeGustavo Alfonso Becquertraduzione di Ileana Schweiger AcutiParmaGuanda1967244 p.19 cmBECQUER,Gustavo Adolfo384820SCHWEIGER ACUTI,IleanaITsalbcISBD990001380330203316II S A 4328550 L.M.II S AVI.5.A. 43(Varie Coll. 134/39)L.M.Varie Coll.BKUMASIAV51020040129USA011551PATRY9020040406USA011737COPAT19020060619USA010957Rimas20099UNISA03148nam 2200505 450 99646442740331620220419114908.0981-15-7877-X10.1007/978-981-15-7877-9(CKB)4100000011994899(DE-He213)978-981-15-7877-9(MiAaPQ)EBC6689292(Au-PeEL)EBL6689292(OCoLC)1263026447(PPN)257354824(EXLCZ)99410000001199489920220419d2021 uy 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierStatistical learning with math and python 100 exercises for building logic /Joe Suzuki1st ed. 2021.Singapore :Springer,[2021]©20211 online resource (XI, 256 p. 446 illus., 170 illus. in color.) 981-15-7876-1 Chapter 1: Linear Algebra -- Chapter 2: Linear Regression -- Chapter 3: Classification -- Chapter 4: Resampling -- Chapter 5: Information Criteria -- Chapter 6: Regularization -- Chapter 7: Nonlinear Regression -- Chapter 8: Decision Trees -- Chapter 9: Support Vector Machine -- Chapter 10: Unsupervised Learning.The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.Mathematical statisticsLogic, Symbolic and mathematicalPython (Computer program language)Mathematical statistics.Logic, Symbolic and mathematical.Python (Computer program language)519.5Suzuki Joe846228MiAaPQMiAaPQMiAaPQBOOK996464427403316Statistical Learning with Math and Python1890233UNISA06080nam 22015013a 450 991036775820332120250203235435.09783039214044303921404710.3390/books978-3-03921-404-4(CKB)4100000010106135(oapen)https://directory.doabooks.org/handle/20.500.12854/42223(ScCtBLL)25e45f2d-06e9-4835-a948-e9739d0876ac(OCoLC)1163837411(oapen)doab42223(EXLCZ)99410000001010613520250203i20192019 uu engurmn|---annantxtrdacontentcrdamediacrrdacarrierBiological CrystallizationJaime Gómez Morales, Juan Manuel García Ruiz, Giuseppe FaliniMDPI - Multidisciplinary Digital Publishing Institute2019Basel, Switzerland :MDPI,2019.1 electronic resource (184 p.)9783039214037 3039214039 For at least six hundred million years, life has been a fascinating laboratory of crystallization, referred to as biomineralization. During this huge lapse of time, many organisms from diverse phyla have developed the capability to precipitate various types of minerals, exploring distinctive pathways for building sophisticated structural architectures for different purposes. The Darwinian exploration was performed by trial and error, but the success in terms of complexity and efficiency is evident. Understanding the strategies that those organisms employ for regulating the nucleation, growth, and assembly of nanocrystals to build these sophisticated devices is an intellectual challenge and a source of inspiration in fields as diverse as materials science, nanotechnology, and biomedicine. However, "Biological Crystallization" is a broader topic that includes biomineralization, but also the laboratory crystallization of biological compounds such as macromolecules, carbohydrates, or lipids, and the synthesis and fabrication of biomimetic materials by different routes. This Special Issue collects 15 contributions ranging from biological and biomimetic crystallization of calcium carbonate, calcium phosphate, and silica-carbonate self-assembled materials to the crystallization of biological macromolecules. Special attention has been paid to the fundamental phenomena of crystallization (nucleation and growth), and the applications of the crystals in biomedicine, environment, and materials science.Biology, life sciencesbicsscchitosanCsep1pbond selection during protein crystallizationbioremediationeducationreductantsheavy metalsbiomimetic crystallizationMTT assayprotein crystallizationdrug discoveryoptimizationpolymyxin resistancelysozymeependymin-related protein (EPDR)equilibration between crystal bond and destructive energiesbarium carbonatedyesmicroseed matrix screeningnanoapatitescolistin resistanceHaloalkane dehalogenasediffusionpolyacrylic acidrandom microseedingprotein ‘affinity’ to waterinsulinprotein crystal nucleationagaroselithium ionsependymin (EPN){00.1} calciteseedingCampylobacter consisusmetallothioneinsCrohn’s diseasebalance between crystal bond energy and destructive surface energiescolor changemicrobially induced calcite precipitation (MICP)crystallization of macromoleculescrystallizationcalceinMCR-1Cry protein crystalsL-tryptophancircular dichroismcrystal violetnanocompositeshalide-binding sitecalcium carbonatePCDAultrasonic irradiationadsorptionbiochemical aspects of the protein crystal nucleationGTL-16 cellsproteinase kneutron protein crystallographyclassical and two-step crystal nucleation mechanismsthermodynamic and energetic approachheavy metal contaminationN-acetyl-D-glucosaminecrystallization in solution flowsolubilitybiomorphsdroplet arraybiomimetic materialsferritinbiomineralizationwastewater treatmentH3O+silicagraphenesupersaturation dependence of the crystal nucleus sizepyrrolemicro-crystalsnucleationcrystallographymammalian ependymin-related protein (MERP)high-throughputvaterite transformationgradientsmaterials sciencebioprecipitationbiomedicinehuman carbonic anhydrase IXprotein crystal nucleation in poresgrowthcrystal growthBiology, life sciencesMorales Jaime Gómez1317916Garcia-Ruiz Juan-ManuelFalini GiuseppeScCtBLLScCtBLLBOOK9910367758203321Biological Crystallization3033089UNINA