01041nam0-2200361---450-99000946729040332120130122132258.0000946729FED01000946729(Aleph)000946729FED0100094672920111026d1954----km-y0itay50------baitaITf-------001yyElementi di parassitologia dell'uomo e degli animali utiliAndrea Scaccini3. ed. riveduta ed ampliataBolognaL. Tinarelli1954292 p., 11 tav.24 cmParassitologia umanaParassitologia animaleScaccini,Andrea74009ITUNINARICAUNIMARCBK990009467290403321C II 23/f862DMVCMII/2/14887DMVMI591.2-3232DMVBFDMVCMDMVMIDMVBFElementi di parassitologia dell'uomo e degli animali utili852846UNINA01092nam a22002651i 450099100203986970753620040120130444.0040407s2001 it |||||||||||||||||eng b12861601-39ule_instARCHE-084122ExLDip.to Scienze StoricheitaA.t.i. Arché s.c.r.l. Pandora Sicilia s.r.l.363.7Pench, Alberto260888Green tax reforms in a computable general equilibrium model for Italy /Alberto PenchMilano :Fondazione ENI Enrico Mattei,200113 p. ;21 cmNote di lavoro della Fondazione ENI Enrico Mattei ;3.2001Politica ambientaleItaliaTasseItalia.b1286160102-04-1416-04-04991002039869707536LE009 GEOG.COLL.14H/312009000316584le009-E0.00-l- 00000.i1342208x16-04-04Green tax reforms in a computable general equilibrium model for Italy302687UNISALENTOle00916-04-04ma -engit 0103075nam 22004933 450 991098466940332120230918084512.097898151369829815136984(CKB)28153922900041(MiAaPQ)EBC30745091(Au-PeEL)EBL30745091(Exl-AI)30745091(OCoLC)1399170449(EXLCZ)992815392290004120230918d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierNumerical Machine Learning1st ed.Sharjah :Bentham Science Publishers,2023.©2023.1 online resource (225 pages)9789815136999 9815136992 Cover -- Title -- Copyright -- End User License Agreement -- Content -- Preface -- Introduction to Machine Learning -- Linear Regression -- Regularization -- Logistic Regression -- Decision Tree -- Gradient Boosting -- Support Vector Machine -- K-means Clustering -- Subject Index Generated by AI.Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. Key features - Provides a concise introduction to numerical concepts in machine learning in simple terms - Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables - Focuses on numerical examples while using small datasets for easy learning - Includes simple Python codes - Includes bibliographic references for advanced reading The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses.Machine learningGenerated by AINumerical analysisGenerated by AIMachine learningNumerical analysisWang Zhiyuan654347Irfan Sayed Ameenuddin1793401Teoh Christopher1793402MiAaPQMiAaPQMiAaPQBOOK9910984669403321Numerical Machine Learning4333097UNINA