Search Results - (((((((ant OR wantsi) OR mantis) OR when) OR cantor) OR anne) OR shared) OR hints) algorithms.

Computational ecology : artificial neural networks and their applications by Zhang, Wenjun

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Deep Learning Quick Reference : Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras. by Bernico, Michael

Table of Contents: “…Cover; Copyright and Credits; Dedication; Packt Upsell; Foreword; Contributors; Table of Contents; Preface; Chapter 1: The Building Blocks of Deep Learning; The deep neural network architectures; Neurons; The neuron linear function; Neuron activation functions; The loss and cost functions in deep learning; The forward propagation process; The back propagation function; Stochastic and minibatch gradient descents; Optimization algorithms for deep learning; Using momentum with gradient descent; The RMSProp algorithm; The Adam optimizer; Deep learning frameworks; What is TensorFlow?…”
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The New Advertising : Branding, Content, and Consumer Relationships in the Data-Driven Social Media Era. by Wang, Ming

Table of Contents: “…Persuasive Avatars: Extending the Self through New Media AdvertisingPart III: Algorithms, Analytics, and Concerns ; 10. Road of Discovery: The Intricacies of Search Marketing; 11. …”
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Microscopic Approaches to Quantum Liquids in Confined Geometries. by Krotscheck, Eckhard

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Advances in parallel computing technologies and applications

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PHealth 2017 : proceedings of the 14th International Conference on Wearable Micro and Nano Technologies for Personalized Health, 14-16 May 2017, Eindhoven, the Netherlands

Table of Contents: “…Information System of Personalized Patient's Adherence Level DeterminationSmartphones to Access to Patient Data in Hospital Settings: Authentication Solutions for Shared Devices; pHealth and Population Health; Architecture for Variable Data Entry into a National Registry; Patient Summaries in Context of Large Scale EHR Networks with Fine Granular Access Control Restrictions; Behavioral Aspects; Classifying Drivers' Cognitive Load Using EEG Signals; Open Dataset for the Automatic Recognition of Sedentary Behaviors.…”
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The bitcoin big bang : how alternative currencies are about to change the world by Kelly, Brian, 1971-

Table of Contents: “…; 8 Building the Nautiluscoin Economy; Dynamic Proof-of-Stake; Nautiluscoin Gross Domestic Product Target; Algorithmic Monetary Policy.…”
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DataFlow supercomputing essentials : research, development and education

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Progress in financial markets research

Table of Contents: “…Concluding Remarks -- Appendix -- References -- Chapter 4: WHEN NONRANDOMNESS APPEARS RANDOM: A CHALLENGE TO FINANCIAL ECONOMICS -- 4.1. …”
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CUDA Application Design and Development. by Farber, Rob

Table of Contents: “…The Nsight Timeline Analysis -- The NVTX Tracing Library -- Scaling Behavior of the CUDA API -- Tuning and Analysis Utilities (TAU) -- Summary -- 4 The CUDA Execution Model -- GPU Architecture Overview -- Thread Scheduling: Orchestrating Performance and Parallelism via the Execution Configuration -- Relevant computeprof Values for a Warp -- Warp Divergence -- Guidelines for Warp Divergence -- Relevant computeprof Values for Warp Divergence -- Warp Scheduling and TLP -- Relevant computeprof Values for Occupancy -- ILP: Higher Performance at Lower Occupancy -- ILP Hides Arithmetic Latency -- ILP Hides Data Latency -- ILP in the Future -- Relevant computeprof Values for Instruction Rates -- Little's Law -- CUDA Tools to Identify Limiting Factors -- The nvcc Compiler -- Launch Bounds -- The Disassembler -- PTX Kernels -- GPU Emulators -- Summary -- 5 CUDA Memory -- The CUDA Memory Hierarchy -- GPU Memory -- L2 Cache -- Relevant computeprof Values for the L2 Cache -- L1 Cache -- Relevant computeprof Values for the L1 Cache -- CUDA Memory Types -- Registers -- Local memory -- Relevant computeprof Values for Local Memory Cache -- Shared Memory -- Relevant computeprof Values for Shared Memory -- Constant Memory -- Texture Memory -- Relevant computeprof Values for Texture Memory -- Global Memory -- Common Coalescing Use Cases -- Allocation of Global Memory -- Limiting Factors in the Design of Global Memory -- Relevant computeprof Values for Global Memory -- Summary -- 6 Efficiently Using GPU Memory -- Reduction -- The Reduction Template -- A Test Program for functionReduce.h -- Results -- Utilizing Irregular Data Structures -- Sparse Matrices and the CUSP Library -- Graph Algorithms -- SoA, AoS, and Other Structures -- Tiles and Stencils -- Summary -- 7 Techniques to Increase Parallelism -- CUDA Contexts Extend Parallelism -- Streams and Contexts.…”
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Principles of artificial neural networks by Graupe, Daniel

Table of Contents: “…Fundamentals of biological neural networks -- ch. 3. Basic principles of ANNs and their early structures. 3.1. Basic principles of ANN design. 3.2. …”
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Communication networks and computer systems : a tribute to Professor Erol Gelenbe

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In pursuit of the traveling salesman : mathematics at the limits of computation by Cook, William, 1957-

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Introduction to dynamics and control of flexible structures by Junkins, John L.

Table of Contents: “…3.2 Lyapunov's Stability Theorem (Direct Method)3.3 Stability of Linear Systems -- Lyapunov Theorem for Linear Systems -- Linear Dynamic Systems Subject to Arbitrary Disturbances -- Stability Analysis for Mechanical Second Order Systems -- 3.4 Nonlinear, Time Varying, and Distributed Parameter Systems -- Local Stability of Linearized Systems -- What to do When U is Only Negative Semi-Definite -- Lyapunov Control Law Design Method -- Work Energy Rate Principle and Laypunov Stable Control Laws -- Globally Stable Tracking Controller: Lyapunov Approach…”
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Developing and validating test items by Haladyna, Thomas M.

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Phase estimation in optical interferometry

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The Oxford guide to library research by Mann, Thomas, 1948-

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Medical Response to Terrorism : Preparedness and Clinical Practice. by Keyes, Daniel C.

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The medical imagination : literature and health in the early United States by Altschuler, Sari

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Advanced Python Programming : Build High Performance, Concurrent, and Multi-Threaded Apps with Python Using Proven Design Patterns. by Lanaro, Gabriele

Table of Contents: “…Cover; Title Page; Copyright; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Benchmarking and Profiling; Designing your application; Writing tests and benchmarks; Timing your benchmark; Better tests and benchmarks with pytest-benchmark; Finding bottlenecks with cProfile; Profile line by line with line_profiler; Optimizing our code; The dis module; Profiling memory usage with memory_profiler; Summary; Chapter 2: Pure Python Optimizations; Useful algorithms and data structures; Lists and deques; Dictionaries; Building an in-memory search index using a hash map; Sets; Heaps…”
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