Deep learning for computer vision with SAS : an introduction /
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...
Clasificación: | Libro Electrónico |
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Autor principal: | |
Formato: | Electrónico eBook |
Idioma: | Inglés |
Publicado: |
Cary, NC :
SAS Institute,
2020.
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Temas: | |
Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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