LEADER 03136oam 2200721I 450 001 9910460240803321 005 20200520144314.0 010 $a1-283-10366-4 010 $a9786613103666 010 $a1-136-80676-8 010 $a0-203-82821-6 024 7 $a10.4324/9780203828212 035 $a(CKB)2670000000082062 035 $a(EBL)668769 035 $a(OCoLC)713021657 035 $a(SSID)ssj0000470016 035 $a(PQKBManifestationID)11299426 035 $a(PQKBTitleCode)TC0000470016 035 $a(PQKBWorkID)10531958 035 $a(PQKB)10533088 035 $a(MiAaPQ)EBC668769 035 $a(PPN)198455496 035 $a(Au-PeEL)EBL668769 035 $a(CaPaEBR)ebr10462640 035 $a(CaONFJC)MIL310366 035 $a(EXLCZ)992670000000082062 100 $a20180706d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aHollywood and the CIA $ecinema, defense, and subversion /$fOliver Boyd-Barrett, David Herrera, and Jim Baumann 210 1$aLondon :$cRoutledge,$d2011. 215 $a1 online resource (225 p.) 225 1 $aMedia, War and Security 300 $aDescription based upon print version of record. 311 $a0-415-83229-2 311 $a0-415-78006-3 320 $aIncludes bibliographical references and index. 327 $aHollywood and the CIA : "a bunch of seedy, squalid bastards" -- The 1960s : in the shadows -- The 1970s : "there are no more secrets" -- The 1980s : "we've wiped out entire cultures! and for what?" -- The 1990s : black ops meet terror -- The 2000s : history interrupted -- Conclusion : once upon a time in Hollywood. 330 $aThis book investigates representations of the Central Intelligence Agency (CIA) in Hollywood films, and the synergies between Hollywood product, U.S. military/defense interests and U.S. foreign policy.As probably the best known of the many different intelligence agencies of the US, the CIA is an exceptionally well known national and international icon or even ""brand,"" one that exercises a powerful influence on the imagination of people throughout the world as well as on the creative minds of filmmakers. The book examines films sampled from five decades - the 1960s, 1970s, 1980s, 19 410 0$aMedia, War and Security 606 $aEspionage in motion pictures 606 $aMotion pictures$xPolitical aspects$zUnited States 606 $aInternational relations in motion pictures 606 $aPolitics in motion pictures 608 $aElectronic books. 615 0$aEspionage in motion pictures. 615 0$aMotion pictures$xPolitical aspects 615 0$aInternational relations in motion pictures. 615 0$aPolitics in motion pictures. 676 $a791.43/6581 700 $aBoyd-Barrett$b Oliver.$0962031 701 $aBaumann$b Jim$01028108 701 $aHerrera$b David$f1986-$01028109 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910460240803321 996 $aHollywood and the CIA$92443959 997 $aUNINA LEADER 03889nam 22006375 450 001 9910409667103321 005 20250609112032.0 010 $a981-15-2910-8 010 $a9789811529108$b(eBook) 024 7 $a10.1007/978-981-15-2910-8 035 $a(CKB)4100000011273628 035 $a(MiAaPQ)EBC6213171 035 $a(DE-He213)978-981-15-2910-8 035 $a(PPN)248393383 035 $a(MiAaPQ)EBC6213130 035 $a(EXLCZ)994100000011273628 100 $a20200529h20202020 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAccelerated optimization for machine learning $efirst-order algorithms /$fZhouchen Lin, Huan Li, Cong Fang 210 1$aSingapore :$cSpringer,$d[2020] 210 4$dİ2020 215 $a1 online resource (286 pages) 311 08$a981-15-2909-4 311 08$a9789811529092 320 $aIncludes bibliographical references and index. 327 $aChapter 1. Introduction -- Chapter 2. Accelerated Algorithms for Unconstrained Convex Optimization -- Chapter 3. Accelerated Algorithms for Constrained Convex Optimization -- Chapter 4. Accelerated Algorithms for Nonconvex Optimization -- Chapter 5. Accelerated Stochastic Algorithms -- Chapter 6. Accelerated Paralleling Algorithms -- Chapter 7. Conclusions. 330 $aThis book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time. 606 $aMachine learning$xMathematics 606 $aMathematical optimization 606 $aComputer science$xMathematics 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 606 $aMath Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17044 606 $aComputational Mathematics and Numerical Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M1400X 615 0$aMachine learning$xMathematics. 615 0$aMathematical optimization. 615 0$aComputer science$xMathematics. 615 14$aMachine Learning. 615 24$aOptimization. 615 24$aMath Applications in Computer Science. 615 24$aComputational Mathematics and Numerical Analysis. 676 $a006.31 700 $aLin$b Zhouchen$4aut$4http://id.loc.gov/vocabulary/relators/aut$0908694 702 $aLi$b Huan$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aFang$b Cong$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910409667103321 996 $aAccelerated Optimization for Machine Learning$92032253 997 $aUNINA