LEADER 02035oam 2200433 a 450 001 9910697493903321 005 20081008162103.0 035 $a(CKB)5470000002387999 035 $a(OCoLC)62469192 035 $a(EXLCZ)995470000002387999 100 $a20051206d2005 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCompilation of geologic, hydrologic, and ground-water flow modeling information for the Spokane Valley--Rathdrum Prairie aquifer, Spokane County, Washington, and Bonner and Kootenai Counties, Idaho$b[electronic resource] /$fby Sue C. Kahl, Rodney R. Caldwell, and James R. Bartolino ; prepared in cooperation with the Idaho Department of Water Resources and the Washington Department of Ecology 210 1$aReston, Va. :$cU.S. Dept. of the Interior, U.S. Geological Survey,$d2005. 215 $aviii, 64 pages $cdigital, PDF file 225 1 $aScientific investigations report ;$v2005-5227 300 $aTitle from PDF title screen (viewed on Dec. 5, 2005). 320 $aIncludes bibliographical references (pages 52-63). 606 $aHydrogeology$zSpokane Valley-Rathdrum Prairie Aquifer (Idaho and Wash.) 606 $aGroundwater flow$zSpokane Valley-Rathdrum Prairie Aquifer (Idaho and Wash.) 615 0$aHydrogeology 615 0$aGroundwater flow 700 $aKahle$b S. C$g(Sue C.)$01382811 701 $aCaldwell$b Rodney R$01394313 701 $aBartolino$b James R$01391155 712 02$aIdaho.$bDepartment of Water Resources. 712 02$aWashington (State).$bDepartment of Ecology. 712 02$aGeological Survey (U.S.) 801 0$bGIS 801 1$bGIS 801 2$bGPO 906 $aBOOK 912 $a9910697493903321 996 $aCompilation of geologic, hydrologic, and ground-water flow modeling information for the Spokane Valley--Rathdrum Prairie aquifer, Spokane County, Washington, and Bonner and Kootenai Counties, Idaho$93451517 997 $aUNINA LEADER 09206nam 2200565 450 001 9910592983203321 005 20230207155555.0 010 $a1-4842-7912-3 024 7 $a10.1007/978-1-4842-7912-0 035 $a(MiAaPQ)EBC7084524 035 $a(Au-PeEL)EBL7084524 035 $a(CKB)24819661800041 035 $a(NjHacI)9924819661800041 035 $a(OCoLC)1344334628 035 $a(OCoLC-P)1344334628 035 $a(PPN)264958837 035 $a(CaSebORM)9781484279120 035 $a(EXLCZ)9924819661800041 100 $a20230207d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPractical MATLAB deep learning $ea projects-based approach /$fMichael Paluszek, Stephanie Thomas, and Eric Ham 205 $aSecond edition. 210 1$aNew York, New York :$cApress Media LLC,$d[2022] 210 4$dİ2022 215 $a1 online resource (338 pages) 225 0 $aITpro collection 311 08$aPrint version: Paluszek, Michael Practical MATLAB Deep Learning Berkeley, CA : Apress L. P.,c2022 9781484279113 320 $aIncludes bibliographical references and index. 327 $aIntro -- Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Preface to the Second Edition -- 1 What Is Deep Learning? -- 1.1 Deep Learning -- 1.2 History of Deep Learning -- 1.3 Neural Nets -- 1.3.1 Daylight Detector -- Problem -- Solution -- How It Works -- 1.3.2 XOR Neural Net -- Problem -- Solution -- How It Works -- 1.4 Deep Learning and Data -- 1.5 Types of Deep Learning -- 1.5.1 Multi-layer Neural Network -- 1.5.2 Convolutional Neural Network (CNN) -- 1.5.3 Recurrent Neural Network (RNN) -- 1.5.4 Long Short-Term Memory Network (LSTM) -- 1.5.5 Recursive Neural Network -- 1.5.6 Temporal Convolutional Machine (TCM) -- 1.5.7 Stacked Autoencoders -- 1.5.8 Extreme Learning Machine (ELM) -- 1.5.9 Recursive Deep Learning -- 1.5.10 Generative Deep Learning -- 1.5.11 Reinforcement Learning -- 1.6 Applications of Deep Learning -- 1.7 Organization of the Book -- 2 MATLAB Toolboxes -- 2.1 Commercial MATLAB Software -- 2.1.1 MathWorks Products -- Deep Learning Toolbox -- Instrument Control Toolbox -- Statistics and Machine Learning Toolbox -- Computer Vision Toolbox -- Image Acquisition Toolbox -- Parallel Computing Toolbox -- Text Analytics Toolbox -- 2.2 MATLAB Open Source -- 2.3 XOR Example -- 2.4 Training -- 2.5 Zermelo's Problem -- 3 Finding Circles -- 3.1 Introduction -- 3.2 Structure -- 3.2.1 imageInputLayer -- 3.2.2 convolution2dLayer -- 3.2.3 batchNormalizationLayer -- 3.2.4 reluLayer -- 3.2.5 maxPooling2dLayer -- 3.2.6 fullyConnectedLayer -- 3.2.7 softmaxLayer -- 3.2.8 classificationLayer -- 3.2.9 Structuring the Layers -- 3.3 Generating Data -- 3.3.1 Problem -- 3.3.2 Solution -- 3.3.3 How It Works -- 3.4 Training and Testing -- 3.4.1 Problem -- 3.4.2 Solution -- 3.4.3 How It Works -- 4 Classifying Movies -- 4.1 Introduction -- 4.2 Generating a Movie Database -- 4.2.1 Problem -- 4.2.2 Solution. 327 $a4.2.3 How It Works -- 4.3 Generating a Viewer Database -- 4.3.1 Problem -- 4.3.2 Solution -- 4.3.3 How It Works -- 4.4 Training and Testing -- 4.4.1 Problem -- 4.4.2 Solution -- 4.4.3 How It Works -- 5 Algorithmic Deep Learning -- 5.1 Building the Filter -- 5.1.1 Problem -- 5.1.2 Solution -- 5.1.3 How It Works -- 5.2 Simulating -- 5.2.1 Problem -- 5.2.2 Solution -- 5.2.3 How It Works -- 5.3 Testing and Training -- 5.3.1 Problem -- 5.3.2 Solution -- 5.3.3 How It Works -- 6 Tokamak Disruption Detection -- 6.1 Introduction -- 6.2 Numerical Model -- 6.2.1 Dynamics -- 6.2.2 Sensors -- 6.2.3 Disturbances -- 6.2.4 Controller -- 6.3 Dynamical Model -- 6.3.1 Problem -- 6.3.2 Solution -- 6.3.3 How It Works -- 6.4 Simulate the Plasma -- 6.4.1 Problem -- 6.4.2 Solution -- 6.4.3 How It Works -- 6.5 Control the Plasma -- 6.5.1 Problem -- 6.5.2 Solution -- 6.5.3 How It Works -- 6.6 Training and Testing -- 6.6.1 Problem -- 6.6.2 Solution -- 6.6.3 How It Works -- 7 Classifying a Pirouette -- 7.1 Introduction -- 7.1.1 Inertial Measurement Unit -- 7.1.2 Physics -- 7.2 Data Acquisition -- 7.2.1 Problem -- 7.2.2 Solution -- 7.2.3 How It Works -- 7.3 Orientation -- 7.3.1 Problem -- 7.3.2 Solution -- 7.3.3 How It Works -- 7.4 Dancer Simulation -- 7.4.1 Problem -- 7.4.2 Solution -- 7.4.3 How It Works -- 7.5 Real-Time Plotting -- 7.5.1 Problem -- 7.5.2 Solution -- 7.5.3 How It Works -- 7.6 Quaternion Display -- 7.6.1 Problem -- 7.6.2 Solution -- 7.6.3 How It Works -- 7.7 Making the IMU Belt -- 7.7.1 Problem -- 7.7.2 Solution -- 7.7.3 How It Works -- 7.8 Testing the System -- 7.8.1 Problem -- 7.8.2 Solution -- 7.8.3 How It Works -- 7.9 Classifying the Pirouette -- 7.9.1 Problem -- 7.9.2 Solution -- 7.9.3 How It Works -- 7.10 Data Acquisition GUI -- 7.10.1 Problem -- 7.10.2 Solution -- 7.10.3 How It Works -- 7.11 Hardware Sources -- 8 Completing Sentences -- 8.1 Introduction. 327 $a8.1.1 Sentence Completion -- 8.1.2 Grammar -- 8.1.3 Sentence Completion by Pattern Recognition -- 8.1.4 Sentence Generation -- 8.2 Generating a Database -- 8.2.1 Problem -- 8.2.2 Solution -- 8.2.3 How It Works -- 8.3 Creating a Numeric Dictionary -- 8.3.1 Problem -- 8.3.2 Solution -- 8.3.3 How It Works -- 8.4 Mapping Sentences to Numbers -- 8.4.1 Problem -- 8.4.2 Solution -- 8.4.3 How It Works -- 8.5 Converting the Sentences -- 8.5.1 Problem -- 8.5.2 Solution -- 8.5.3 How It Works -- 8.6 Training and Testing -- 8.6.1 Problem -- 8.6.2 Solution -- 8.6.3 How It Works -- 9 Terrain-Based Navigation -- 9.1 Introduction -- 9.2 Modeling Our Aircraft -- 9.2.1 Problem -- 9.2.2 Solution -- 9.2.3 How It Works -- 9.3 Generating Terrain -- 9.3.1 Problem -- 9.3.2 Solution -- 9.3.3 How It Works -- 9.4 Close-Up Terrain -- 9.4.1 Problem -- 9.4.2 Solution -- 9.4.3 How It Works -- 9.5 Building the Camera Model -- 9.5.1 Problem -- 9.5.2 Solution -- 9.5.3 How It Works -- 9.6 Plotting the Trajectory -- 9.6.1 Problem -- 9.6.2 Solution -- 9.6.3 How It Works -- 9.7 Creating the Training Images -- 9.7.1 Problem -- 9.7.2 Solution -- 9.7.3 How It Works -- 9.8 Training and Testing -- 9.8.1 Problem -- 9.8.2 Solution -- 9.8.3 How It Works -- 9.9 Simulation -- 9.9.1 Problem -- 9.9.2 Solution -- 9.9.3 How It Works -- 10 Stock Prediction -- 10.1 Introduction -- 10.2 Generating a Stock Market -- 10.2.1 Problem -- 10.2.2 Solution -- 10.2.3 How It Works -- 10.3 Creating a Stock Market -- 10.3.1 Problem -- 10.3.2 Solution -- 10.3.3 How It Works -- 10.4 Training and Testing -- 10.4.1 Problem -- 10.4.2 Solution -- 10.4.3 How It Works -- 11 Image Classification -- 11.1 Introduction -- 11.2 Using AlexNet -- 11.2.1 Problem -- 11.2.2 Solution -- 11.2.3 How It Works -- 11.3 Using GoogLeNet -- 11.3.1 Problem -- 11.3.2 Solution -- 11.3.3 How It Works -- 12 Orbit Determination -- 12.1 Introduction. 327 $a12.2 Generating the Orbits -- 12.2.1 Problem -- 12.2.2 Solution -- 12.2.3 How It Works -- 12.3 Training and Testing -- 12.3.1 Problem -- 12.3.2 Solution -- 12.3.3 How It Works -- 12.4 Implementing an LSTM -- 12.4.1 Problem -- 12.4.2 Solution -- 12.4.3 How It Works -- 13 Earth Sensors -- 13.1 Introduction -- 13.2 Linear Output Earth Sensor -- 13.2.1 Problem -- 13.2.2 Solution -- 13.2.3 How It Works -- 13.3 Segmented Earth Sensor -- 13.3.1 Problem -- 13.3.2 Solution -- 13.3.3 How It Works -- 13.4 Linear Output Sensor Neural Network -- 13.4.1 Problem -- 13.4.2 Solution -- 13.4.3 How It Works -- 13.5 Segmented Sensor Neural Network -- 13.5.1 Problem -- 13.5.2 Solution -- 13.5.3 How It Works -- 14 Generative Modeling of Music -- 14.1 Introduction -- 14.2 Generative Modeling Description -- 14.3 Problem: Music Generation -- 14.4 Solution -- 14.5 Implementation -- 14.6 Alternative Methods -- 15 Reinforcement Learning -- 15.1 Introduction -- 15.2 Titan Lander -- 15.3 Titan Atmosphere -- 15.3.1 Problem -- 15.3.2 Solution -- 15.3.3 How It Works -- 15.4 Simulating the Aircraft -- 15.4.1 Problem -- 15.4.2 Solution -- 15.4.3 How It Works -- 15.5 Simulating Level Flight -- 15.5.1 Problem -- 15.5.2 Solution -- 15.5.3 How It Works -- 15.6 Optimal Trajectory -- 15.6.1 Problem -- 15.6.2 Solution -- 15.6.3 How It Works -- 15.7 Reinforcement Example -- 15.7.1 Problem -- 15.7.2 Solution -- 15.7.3 How It Works -- Bibliography -- Index. 330 $aHarness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, you'll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning. 606 $aMachine learning 615 0$aMachine learning. 676 $a006.31 700 $aPaluszek$b Michael$0887778 702 $aHam$b Eric 702 $aThomas$b Stephanie 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910592983203321 996 $aPractical MATLAB Deep Learning$92502711 997 $aUNINA LEADER 01598nam0 22003253i 450 001 UBO0010842 005 20251003044417.0 010 $a8885040357 100 $a20090127d1991 ||||0itac50 ba 101 | $aita 102 $ait 181 1$6z01$ai $bxxxe 182 1$6z01$an 200 1 $aLaboratorio di meccanica razionale$eesercizi e temi d'esame$fA. Muracchini, T. Ruggeri, L. 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