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LEADER 00000cam  2200553   4500 
001    1159041705 
003    OCoLC 
005    20201009145540.5 
006    m        d         
007    cr ||||||||||| 
008    200621s2020    ncu     o     000 0 eng d 
019    1158802866|a1159170246|a1179125948 
020    1642959162|q(electronic bk.) 
020    9781642959161|q(electronic bk.) 
020    1642959170|q(electronic bk.) 
020    9781642959178|q(electronic bk.) 
035    (OCoLC)1159041705|z(OCoLC)1158802866|z(OCoLC)1159170246
       |z(OCoLC)1179125948 
040    YDX|beng|erda|cYDX|dEBLCP|dN$T|dUAB|dOCLCF|dNJT 
049    MAIN 
050  4 Q325.5 
082 04 006.3/1|223 
100 1  Blanchard, Robert|c(Data scientist),|0http://id.loc.gov/
       authorities/names/no2020114038|eauthor. 
245 10 Deep learning for computer vision with SAS :|ban 
       introduction /|cRobert Blanchard. 
264  1 Cary, NC :|bSAS Institute,|c2020. 
300    1 online resource 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    text file|2rdaft|0http://rdaregistry.info/termList/
       fileType/1002 
505 0  Intro -- Contents -- About This Book -- What Does This 
       Book Cover? -- Is This Book for You? -- What Should You 
       Know about the Examples? -- Software Used to Develop the 
       Book's Content -- Example Code and Data -- We Want to Hear
       from You -- About The Author -- Introduction to Deep 
       Learning -- Introduction to Neural Networks -- Biological 
       Neurons -- Mathematical Neurons -- Figure 1.1: Multilayer 
       Perceptron -- Deep Learning -- Table 1.1: Traditional 
       Neural Networks versus Deep Learning -- Figure 1.2: 
       Hyperbolic Tangent Function -- Figure 1.3: Rectified 
       Linear Function 
505 8  Figure 1.4: Exponential Linear Function -- Batch Gradient 
       Descent -- Figure 1.5: Batch Gradient Descent -- 
       Stochastic Gradient Descent -- Figure 1.6: Stochastic 
       Gradient Descent -- Introduction to ADAM Optimization -- 
       Weight Initialization -- Figure 1.7: Constant Variance 
       (Standard Deviation = 1) -- Figure 1.8: Constant Variance 
       (Standard Deviation =,, -- + ..≈. ) -- 
       Regularization -- Figure 1.9: Regularization Techniques --
       Batch Normalization -- Batch Normalization with Mini-
       Batches -- Traditional Neural Networks versus Deep 
       Learning 
505 8  Table 1.2: Comparison of Central Processing Units and 
       Graphical Processing Units -- Deep Learning Actions -- 
       Building a Deep Neural Network -- Table 1.3: Layer Types -
       - Training a Deep Learning CAS Action Model -- 
       Demonstration 1: Loading and Modeling Data with 
       Traditional Neural Network Methods -- Table 1.4: Develop 
       Data Set Variables -- Figure 1.10: Results of the FREQ 
       Procedure -- Figure 1.11: Results of the NNET Procedure --
       Figure 1.12: Score Information -- Demonstration 2: 
       Building and Training Deep Learning Neural Networks Using 
       CASL Code 
505 8  Figure 1.13: Transcription of the Model Architecture -- 
       Figure 1.14: Model Shell and Layer Information -- Figure 
       1.15: Model Information -- Figure 1.15: Optimization 
       History Table -- Figure 1.16: Model Information Details --
       Convolutional Neural Networks -- Introduction to 
       Convoluted Neural Networks -- Input Layers -- Figure 2.1: 
       Convolutional Neural Network -- Figure 2.2: Grayscale 
       Image Channel -- Figure 2.3: Color Image Channels -- 
       Convolutional Layers -- Figure 2.4: Single-channel 
       Convolution Without Kernel Flipping -- Using Filters -- 
       Figure 2.5: Starting Position of the Filter 
505 8  Figure 2.6: Products of the Entries Between the Filter and
       Input -- Figure 2.7: Range Movement Due to STRIDE 
       Hyperparameter -- Figure 2.8: Feature Map with Filter 
       Response at Every Spatial Position -- Figure 2.9: Filter 
       Weights and Nonlinear Transformation -- Padding -- Figure 
       2.10: Feature Map Without Padding -- Figure 2.11: Feature 
       Map with Padding -- Figure 2.12: Without Padding -- Figure
       2.13: Automatic Padding with SAS -- Figure 2.14: SAS 
       Automatically Adjusts for Non-Integer Feature Maps -- 
       Feature Map Dimensions -- Figure 2.15: Feature Map 
       Dimensions -- Pooling Layers 
520    Discover deep learning and computer vision with SAS! Deep 
       Learning for Computer Vision with SAS: An Introduction 
       introduces the pivotal components of deep learning. 
       Readers will gain an in-depth understanding of how to 
       build deep feedforward and convolutional neural networks, 
       as well as variants of denoising autoencoders. Transfer 
       learning is covered to help readers learn about this 
       emerging field. Containing a mix of theory and application,
       this book will also briefly cover methods for customizing 
       deep learning models to solve novel business problems or 
       answer research questions. SAS program. 
630 00 SAS (Computer file)|0http://id.loc.gov/authorities/names/
       n88028236 
650  0 Machine learning.|0http://id.loc.gov/authorities/subjects/
       sh85079324 
650  0 Computer vision.|0http://id.loc.gov/authorities/subjects/
       sh85029549 
776 08 |iPrint version:|aBlanchard, Robert|tDeep Learning for 
       Computer Vision with SAS : An Introduction|dCary, NC : SAS
       Institute,c2020|z9781642959154. 
990    ProQuest Safari|bO'Reilly Online Learning: Academic/Public
       Library Edition|c2020-10-09|yKB collection name change
       |5OH1 
990    ProQuest Safari|bO'Reilly Safari Learning Platform: 
       Academic edition|c2020-09-25|yMaster record encoding level
       change|5OH1 
990    ProQuest Safari|bO'Reilly Safari Learning Platform: 
       Academic edition|c2020-08-28|yMaster record variable 
       field(s) change: 505 - OCLC control number change|5OH1 
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