LEADER 01365cam0-22003851i-450- 001 990000980040403321 005 20060918113748.0 010 $a0-444-70520-1 035 $a000098004 035 $aFED01000098004 035 $a(Aleph)000098004FED01 035 $a000098004 100 $a20001205d1989----km-y0itay50------ba 101 0 $aeng 102 $aNL 105 $a--------101yy 200 1 $aLogic, Methodology and Philosophy of Science 8.$eproceedings of Eighth international congress of logic, methodology and philosophy of science, Moscow, 1987$fedited by Jens Erik Fenstad, Ivan T. Frolov, Risto Hilpinen 210 $aAmsterdam [etc.]$cNorth-Holland$d1989 215 $aXVII, 702 p.$d23 cm 225 1 $aStudies in logic and the foundations of mathematics$v126 610 0 $aFondamenti della logica matematica e probabilità 676 $a519 702 1$aFenstad,$bJens Erik 702 1$aFrolov,$bIvan T. 702 1$aHilpinen,$bRisto 710 12$aInternational congress of logic, methodology, and philosophy of science$d<8. ;$f1987 ;$eMosca>$0425665 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000980040403321 952 $a4A-114.003$b17965$fFI1 952 $a121-G-37$b8727$fMA1 959 $aFI1 959 $aMA1 996 $aLogic, Methodology and Philosophy of Science 8$9353285 997 $aUNINA LEADER 04087nam 2200481I 450 001 9910795281003321 005 20230124202106.0 010 $a1-83882-572-X 035 $a(CKB)4920000000457578 035 $a(OCoLC)1181958606 035 $a(OCoLC)on1181958606 035 $a(MiAaPQ)EBC6126530 035 $a(CaSebORM)9781838821654 035 $a(PPN)243777736 035 $a(EXLCZ)994920000000457578 100 $a20200803d2020 uy 0 101 0 $aeng 135 $aurunu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced deep learning with TensorFlow 2 and Keras $eapply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more /$fRowel Atienza 205 $aSecond edition. 210 1$aBirmingham, UK :$cPackt Publishing,$d2020. 215 $a1 online resource (1 volume) $cillustrations 311 $a1-83882-165-1 320 $aIncludes bibliographical references and index. 330 $aUpdated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs Understand deep neural networks - including ResNet and DenseNet Understand and build autoregressive models ? autoencoders, VAEs, and GANs Discover and implement deep reinforcement learning methods Who this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be hel... 606 $aArtificial intelligence 606 $aMachine learning 606 $aPython (Computer program language) 606 $aNeural networks (Computer science) 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aPython (Computer program language) 615 0$aNeural networks (Computer science) 700 $aAtienza$b Rowel$01527424 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910795281003321 996 $aAdvanced deep learning with TensorFlow 2 and Keras$93770225 997 $aUNINA