03766nam a22004097i 4500999001700000005001700017008004100034020001700075040003100092041000800123043000800131082002100139100002600160245007300186264005600259300004600315336002400361337002500385338002400410490005300434504002700487505060100514506001501115520055801130520085701688520028802545650004302833650005502876650004902931650004402980650004103024650004003065700003203105700003403137942001203171952017303183 c76065d7606520180607162641.0180309b2016 xxu||||fr|||| 001 0 eng d a978062035613 aCO-UCACbengcCO-UCACerda0 beng aenk04222a006.31cG6511 aGoodfellow, Ian9263210aDepp learning / cIan Gioodfellow, Yoshua Bengio and Aaron Courville 1aCambridge, Massachusetts : bThe MIT Press, c2016. a766 páginas ; c18 x 24 cm.f1 ejemplar 2rdacontenidoaTexto 2rdamedioaNo mediado 2rdasoporteaVolumen0 aAdaptive computation and machine learning series aIncluye bibliografía aIntroduction. Applied math and machine learning basics. Probability and information theory. Numerical computation. Machine learning basics. Deep networks: modern practices. Regularization for deep learning. Optimization for training deep models. Convolutional networks. Sequence modeling: recurrent and recursive nets. Practical methodology. Applications. Deep learning research. Linear factor models. Autoencoders. Representation learning. Structured probabilistic models for deep learning. Monte Carlo methods. Confronting the partition function. Approximate inference. Deep generative models. aIndustrial3 aDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. 3 aThe text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
3 aDeep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
042ArmarcaAPRENDIZAJE AUTOMÁTICO92633072ArmarcaPROCESAMIENTO ELECTRÓNICO DE DATOS92544072ArmartaPROGRAMACIÓN (MATEMÁTICAS)92604072ArmarcaSISTEMAS HOMBRE-MÁQUINA92634072ArmarcaMÉTODO DE MONTECARLO92637072ArmarcaMODELOS MATEMÁTICOS926381 aBengio, Yoshuaeautor926351 aCourville, Aaroneautor92636 2ddccBK 00102ddc3Industrial4050708GENa001b001cCPd2018-01-23eLibrería Mundo Técnicofcg306000.00i057l3o006.31 / G651p59167r2018-05-30s2018-05-16tEj. 1yBK