LEADER 03299nam 2200469 450 001 9910483468303321 005 20210319080836.0 010 $a1-4842-6150-X 024 7 $a10.1007/978-1-4842-6150-7 035 $a(CKB)4100000011585952 035 $a(DE-He213)978-1-4842-6150-7 035 $a(MiAaPQ)EBC6403739 035 $a(CaSebORM)9781484261507 035 $a(PPN)252510763 035 $a(EXLCZ)994100000011585952 100 $a20210319d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aArtificial neural networks with TensorFlow 2 $eANN architecture machine learning projects /$fPoornachandra Sarang 205 $a1st ed. 2021. 210 1$a[Place of publication not identified] :$cApress,$d[2021] 210 4$dİ2021 215 $a1 online resource (XXIX, 726 p. 237 illus.) 311 $a1-4842-6149-6 327 $aChapter 1: TensorFlow Jump Start -- Chapter 2: A Closer Look at TensorFlow -- Chapter 3: Deep Dive in tf.keras -- Chapter 4: Transfer Learning -- Chapter 5: Neutral Networks for Regression -- Chapter 6: Estimators -- Chapter 7: Text Generation -- Chapter 8: Language Translation -- Chapter 9: Natural Langauge -- Chapter 10: Image Captioning -- Chapter 11: Time Series -- Chapter 12: Style Transfer -- Chapter 13: Image Generation- Chapter 14: Image Translation. 330 $aDevelop machine learning models across various domains. This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects. After learning what's new in TensorFlow 2, you'll dive right into developing machine learning models through applicable projects. This book covers a wide variety of ANN architectures?starting from working with a simple sequential network to advanced CNN, RNN, LSTM, DCGAN, and so on. A full chapter is devoted to each kind of network and each chapter consists of a full project describing the network architecture used, the theory behind that architecture, what data set is used, the pre-processing of data, model training, testing and performance optimizations, and analysis. This practical approach can either be used from the beginning through to the end or, if you're already familiar with basic ML models, you can dive right into the application that interests you. Line-by-line explanations on major code segments help to fill in the details as you work and the entire project source is available to you online for learning and further experimentation. With Artificial Neural Networks with TensorFlow 2 you'll see just how wide the range of TensorFlow's capabilities are. You will: Develop Machine Learning Applications Translate languages using neural networks Compose images with style transfer. 606 $aNeural networks (Computer science) 606 $aMachine learning 615 0$aNeural networks (Computer science) 615 0$aMachine learning. 676 $a006.32 700 $aSarang$b Poornachandra$0476229 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483468303321 996 $aArtificial neural networks with TensorFlow 2$92849390 997 $aUNINA