001449485 000__ 05362cam\a2200577\i\4500
001449485 001__ 1449485
001449485 003__ OCoLC
001449485 005__ 20230310004402.0
001449485 006__ m\\\\\o\\d\\\\\\\\
001449485 007__ cr\cn\nnnunnun
001449485 008__ 220913s2022\\\\nyua\\\\ob\\\\001\0\eng\d
001449485 019__ $$a1344323732$$a1346260257
001449485 020__ $$a9781484279120$$qelectronic book
001449485 020__ $$a1484279123$$qelectronic book
001449485 020__ $$z9781484279113
001449485 020__ $$z1484279115
001449485 0247_ $$a10.1007/978-1-4842-7912-0$$2doi
001449485 035__ $$aSP(OCoLC)1344334628
001449485 040__ $$aORMDA$$beng$$erda$$epn$$cORMDA$$dEBLCP$$dGW5XE$$dYDX$$dN$T$$dOCLCF$$dYDX$$dOCLCQ
001449485 049__ $$aISEA
001449485 050_4 $$aQ325.5$$b.P35 2022
001449485 08204 $$a006.3/1$$223/eng/20220913
001449485 1001_ $$aPaluszek, Michael,$$eauthor.
001449485 24510 $$aPractical MATLAB deep learning :$$ba projects-based approach /$$cMichael Paluszek, Stephanie Thomas and Eric Ham.
001449485 250__ $$aSecond edition.
001449485 264_1 $$aNew York, NY :$$bApress,$$c[2022]
001449485 300__ $$a1 online resource (338 pages) :$$billustrations
001449485 336__ $$atext$$btxt$$2rdacontent
001449485 337__ $$acomputer$$bc$$2rdamedia
001449485 338__ $$aonline resource$$bcr$$2rdacarrier
001449485 504__ $$aIncludes bibliographical references and index.
001449485 5050_ $$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)
001449485 5058_ $$a1.5.5 Recursive Neural Network -- 1.5.6 Temporal Convolutional Machine (TCM) -- 1.5.7 Stacked Autoencoders -- 1.5.8 Extreme Learning Machine (ELM) -- 1.5.9 Recursive Deep Learning -- 1.5.10 Generative Deep Learning -- 1.5.11 Reinforcement Learning -- 1.6 Applications of Deep Learning -- 1.7 Organization of the Book -- 2 MATLAB Toolboxes -- 2.1 Commercial MATLAB Software -- 2.1.1 MathWorks Products -- Deep Learning Toolbox -- Instrument Control Toolbox -- Statistics and Machine Learning Toolbox -- Computer Vision Toolbox -- Image Acquisition Toolbox -- Parallel Computing Toolbox
001449485 5058_ $$aText 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
001449485 5058_ $$a4.1 Introduction -- 4.2 Generating a Movie Database -- 4.2.1 Problem -- 4.2.2 Solution -- 4.2.3 How It Works -- 4.3 Generating a Viewer Database -- 4.3.1 Problem -- 4.3.2 Solution -- 4.3.3 How It Works -- 4.4 Training and Testing -- 4.4.1 Problem -- 4.4.2 Solution -- 4.4.3 How It Works -- 5 Algorithmic Deep Learning -- 5.1 Building the Filter -- 5.1.1 Problem -- 5.1.2 Solution -- 5.1.3 How It Works -- 5.2 Simulating -- 5.2.1 Problem -- 5.2.2 Solution -- 5.2.3 How It Works -- 5.3 Testing and Training -- 5.3.1 Problem -- 5.3.2 Solution -- 5.3.3 How It Works -- 6 Tokamak Disruption Detection
001449485 5058_ $$a6.1 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
001449485 506__ $$aAccess limited to authorized users.
001449485 520__ $$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.
001449485 588__ $$aDescription based on online resource; title from digital title page (viewed on November 08, 2022).
001449485 63000 $$aMATLAB.
001449485 650_0 $$aMachine learning.
001449485 655_0 $$aElectronic books.
001449485 7001_ $$aThomas, Stephanie J.,$$eauthor.
001449485 7001_ $$aHam, Eric,$$eauthor.
001449485 77608 $$iPrint version: $$z1484279115$$z9781484279113$$w(OCoLC)1274198335
001449485 852__ $$bebk
001449485 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-7912-0$$zOnline Access$$91397441.1
001449485 909CO $$ooai:library.usi.edu:1449485$$pGLOBAL_SET
001449485 980__ $$aBIB
001449485 980__ $$aEBOOK
001449485 982__ $$aEbook
001449485 983__ $$aOnline
001449485 994__ $$a92$$bISE