دانلود کتاب Deep Learning for Numerical Applications with SAS®

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Contents Preface About This Book What Does This Book Cover? Is This Book for You? What Are the Prerequisites for This Book? 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 Acknowledgments Chapter 1: Introduction Deep Learning Is Deep Learning for You? It’s All about Performance Flynn’s Taxonomy Life after Flynn Organization of This Book Chapter 2: Deep Learning Deep Learning Connectionism The Perceptron The First AI Winter The Experts to the Rescue The Second AI Winter The Deeps The Third AI Winter Some Supervision Required A Few Words about CAS Deployment Models CAS Sessions Caslibs Workers Action Sets and Actions Cleanup All about the Data The Men Body Mass Index Data Set The IRIS Data Set Logistic Regression Preamble Create the ANN Training Inference Conclusion Chapter 3: Regressions A Brief History of Regressions All about the Data (Reprise) The CARS Data Set A Simple Regression The Universal Approximation Theorem Universal Approximation Framework Approximation of a Continuous Function Conclusions Chapter 4: Many-Task Computing A Taxonomy for Parallel Programs Tasks Are the New Threads What Is a Task? Inputs and Outputs Immutable Inputs What Is a Job Flow? Examples of Job Flows Mutable Inputs Task Revisited Partitioning Federated Areas Persistent Area Caveats and Pitfalls Not Declaring Your Inputs Not Treating Your Immutable Inputs as Immutable Not Declaring Your Outputs Performance of Grid Scheduling Data-Object Pooling Portable Learning Conclusion Chapter 5: Monte Carlo Simulations Monte Carlo or Las Vegas? Random Walk Multi-threaded Random Walk SAS Studio Live ETL A Parallel Program A Parallel Program with Partitions Many Cores Conclusion Chapter 6: GPU History of GPUs The Golden Age of the Multicore The Golden Age of the Graphics Card The Golden Age of the GPU The CUDA Programming Model Hello The CUDA Toolkit Buffon Revisited Generating Random Walk Data with CUDA Putting It All Together Conclusion Chapter 7: Monte Carlo Simulations with Deep Learning Generating Data Training Data Testing Data Training the Network Inference Using the Network Performance Summary Other Examples Pricing of American Options Pricing of Variable Annuities Contracts Conclusion Chapter 8: Deep Learning for Numerical Applications in the Enterprise Enterprise Applications A Task Data Task Implementation A Simple Flow A Training Flow Task An Inference Flow Documentation Heterogeneous Architectures Collaboration with Federated Areas Deploying DL with Federated Areas Conclusions Chapter 9: Conclusions Data-Driven Programming The Quest for Speed From Tasks to GPUs Training and Inference FPGA Hybrid Architectures Appendix A: Development Environment Setup LINUX Windows References Index