LEADER 02441nam 2200565 450 001 9910460914703321 005 20200520144314.0 010 $a1-4438-7496-5 035 $a(CKB)3710000000355232 035 $a(EBL)2076586 035 $a(OCoLC)905864035 035 $a(SSID)ssj0001467759 035 $a(PQKBManifestationID)11837073 035 $a(PQKBTitleCode)TC0001467759 035 $a(PQKBWorkID)11519669 035 $a(PQKB)10056362 035 $a(MiAaPQ)EBC2076586 035 $a(Au-PeEL)EBL2076586 035 $a(CaPaEBR)ebr11019501 035 $a(CaONFJC)MIL720232 035 $a(EXLCZ)993710000000355232 100 $a20150218h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aArt and social justice $emedia collection /$fedited by Mike Hajimichael 210 1$aNewcastle upon Tyne, England :$cCambridge Scholars Publishing,$d2015. 210 4$dİ2015 215 $a1 online resource (135 p.) 300 $aDescription based upon print version of record. 311 $a1-322-88950-3 311 $a1-4438-7196-6 327 $a""TABLE OF CONTENTS""; ""INTRODUCTION""; ""CHAPTER ONE""; ""CHAPTER TWO""; ""CHAPTER THREE""; ""CHAPTER FOUR""; ""CHAPTER FIVE""; ""CHAPTER SIX""; ""CHAPTER SEVEN""; ""CHAPTER EIGHT""; ""CHAPTER NINE""; ""CHAPTER TEN""; ""CONTRIBUTORS"" 330 $aThis book is a collection of articles that reflect on various connectivities between art and social justice and media which are pertinent to studying contemporary societies. How different forms of media and art, in the broadest possible meaning of these terms, reflect on, relate to, and campaign for social justice is an important topic to consider as artists, academics and activists. The subject matter of the book is also contextualized, with attention being paid to historical, cultural and communication factors, and with chapters referencing situations and collaborations in Brazil, Cyprus, G 606 $aEducation$xSocial aspects 606 $aArts$xStudy and teaching 608 $aElectronic books. 615 0$aEducation$xSocial aspects. 615 0$aArts$xStudy and teaching. 676 $a306.43 702 $aHajimichael$b Mike 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910460914703321 996 $aArt and social justice$92259721 997 $aUNINA LEADER 01140nam a2200277 i 4500 001 991003404909707536 005 20020509121555.0 008 000229s1980 it ||| | ita 035 $ab11156508-39ule_inst 035 $aPARLA181208$9ExL 040 $aDip.to Scienze Storiche Fil. e Geogr.$bita 082 0 $a904 100 1 $aDe Laugier, Cesare$0315031 245 14$aGli italiani in Russia :$b1812, 1941-1943 260 $aMilano :$bMursia,$c1980 300 $a245 p. :$btav. ;$c21 cm. 490 0 $aTestimonianze ;$v112 500 $aContiene una parte dell'opera Gli Italini in russia, di Cesare De Laugier, pubbl. a Firenze nel 1826-1827, e Gli italiani nella campagna di Russia dal 1941 al 1943, di Giulio Bedeschi. 500 $aIn testa al front.: Cesare De Laugier, Giulio Bedeschi. 700 1 $aBedeschi, Giulio 907 $a.b11156508$b23-02-17$c28-06-02 912 $a991003404909707536 945 $aLE009 STOR.75-79$g1$i2009000044067$lle009$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i1129940x$z28-06-02 996 $aItaliani in Russia$9872882 997 $aUNISALENTO 998 $ale009$b01-01-00$cm$da $e-$fita$git $h4$i1 LEADER 03845nam 2200481 450 001 9910825818003321 005 20230530205623.0 010 $a1-78913-186-3 035 $a(CKB)4100000005116231 035 $a(Au-PeEL)EBL5446051 035 $a(CaPaEBR)ebr11590663 035 $a(OCoLC)1045010181 035 $a(CaSebORM)9781789138139 035 $a(MiAaPQ)EBC5446051 035 $a(PPN)233397248 035 $a(EXLCZ)994100000005116231 100 $a20180808d2018 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLearn Unity ML-Agents $efundamentals of Unity machine learning : incorporate new powerful ML algorithms such as deep reinforcement learning for games /$fMichael Lanham 205 $a1st edition 210 1$aBirmingham ;$aMumbai :$cPackt,$d2018. 215 $a1 online resource (197 pages) $cillustrations 311 $a1-78913-813-2 330 $aTransform games into environments using machine learning and Deep learning with Tensorflow, Keras, and Unity About This Book Learn how to apply core machine learning concepts to your games with Unity Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply them to your games Learn How to build multiple asynchronous agents and run them in a training scenario Who This Book Is For This book is intended for developers with an interest in using Machine learning algorithms to develop better games and simulations with Unity. The reader will be required to have a working knowledge of C# and a basic understanding of Python. What You Will Learn Develop Reinforcement and Deep Reinforcement Learning for games. Understand complex and advanced concepts of reinforcement learning and neural networks Explore various training strategies for cooperative and competitive agent development Adapt the basic script components of Academy, Agent, and Brain to be used with Q Learning. Enhance the Q Learning model with improved training strategies such as Greedy-Epsilon exploration Implement a simple NN with Keras and use it as an external brain in Unity Understand how to add LTSM blocks to an existing DQN Build multiple asynchronous agents and run them in a training scenario In Detail Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API. This book takes you from the basics of Reinforcement and Q Learning to building Deep Recurrent Q-Network agents that cooperate or compete in a multi-agent ecosystem. You will start with the basics of Reinforcement Learning and how to apply it to problems. Then you will learn how to build self-learning advanced neural networks with Python and Keras/TensorFlow. From there you move o n to more advanced training scenarios where you will learn further innovative ways to train your network with A3C, imitation, and curriculum learning models. By the end of the book, you will have learned how to build more complex environments by building a cooperative and competitive multi-agent ecosystem. Style and approach This book focuses on the foundations of ML, RL and DL for building agents in a game or simulation 606 $aVideo games$xProgramming 606 $aMachine learning 606 $aApplication software$xDevelopment 615 0$aVideo games$xProgramming. 615 0$aMachine learning. 615 0$aApplication software$xDevelopment. 676 $a794.81526 700 $aLanham$b Micheal$0883448 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910825818003321 996 $aLearn Unity ML-Agents$94104994 997 $aUNINA