LEADER 04134nam 22005655 450 001 9910300746503321 005 20200706203749.0 010 $a9781484234532 010 $a1484234537 024 7 $a10.1007/978-1-4842-3453-2 035 $a(CKB)4100000004243391 035 $a(DE-He213)978-1-4842-3453-2 035 $a(MiAaPQ)EBC5379938 035 $a(CaSebORM)9781484234532 035 $a(PPN)22740694X 035 $a(OCoLC)1038280664 035 $a(OCoLC)on1038280664 035 $a(EXLCZ)994100000004243391 100 $a20180502d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntroduction to Deep Learning Business Applications for Developers $eFrom Conversational Bots in Customer Service to Medical Image Processing /$fby Armando Vieira, Bernardete Ribeiro 205 $a1st ed. 2018. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2018. 215 $a1 online resource (XXI, 343 p. 64 illus.) 311 08$a9781484234525 311 08$a1484234529 320 $aIncludes bibliographical references. 327 $a1 Introduction -- 2 Deep Learning: An Overview -- 3 Deep Neural Network Models -- 4 Image Processing -- 5 Natural Language Processing and Speech -- 6 Reinforcement Learning and Robotics -- 7 Recommendations Algorithms and Advertising -- 8 Games and Art -- 9 Other Applications -- 10 Business Impact of DL Technology -- 11 New Research and Future Directions -- Appendix Training DNN with Keras. 330 $aDiscover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles. An Introduction to Deep Learning Business Applications for Developers covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. You?ll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets). You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer. After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. The book contains some coding examples, tricks, and insights on how to train deep learning models using the Keras framework. You will: Find out about deep learning and why it is so powerful Work with the major algorithms available to train deep learning models See the major breakthroughs in terms of applications of deep learning Run simple examples with a selection of deep learning libraries Discover the areas of impact of deep learning in business. 606 $aArtificial intelligence 606 $aPython (Computer program language) 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aPython$3https://scigraph.springernature.com/ontologies/product-market-codes/I29080 615 0$aArtificial intelligence. 615 0$aPython (Computer program language) 615 14$aArtificial Intelligence. 615 24$aPython. 676 $a006 700 $aVieira$b Armando$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063726 702 $aRibeiro$b Bernardete$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910300746503321 996 $aIntroduction to Deep Learning Business Applications for Developers$92534263 997 $aUNINA