Search Results - (((((((kent OR pants) OR wanton) OR makant) OR cantor) OR anne) OR shared) OR hints) algorithms.

Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition. by Zaccone, Giancarlo

Table of Contents: “…; Artificial neural networks; The biological neurons; The artificial neuron; How does an ANN learn?; ANNs and the backpropagation algorithm; Weight optimization; Stochastic gradient descent; Neural network architectures; Deep Neural Networks (DNNs); Multilayer perceptron; Deep Belief Networks (DBNs).…”
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Advanced reliability modeling : part II

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Environmental modeling with GIS

Table of Contents: “…Mapping of the Dust Emission Sources -- 6.2. Sharing Data between Dispersion Modeling Tools and GIS -- 6.3. …”
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Advanced Artificial Intelligence.

Table of Contents: “…3.9 Influence-based Backjumping3.10 Constraint Relation Processing; 3.10.1 Unit Sharing Strategy for Identical Relation; 3.10.2 Interval Propagation; 3.10.3 Inequality Graph; 3.10.4 Inequality Reasoning; 3.11 Constraint Reasoning System COPS; 3.12 ILOG Solver; Exercise; Chapter 4 Qualitative Reasoning; 4.1 Introduction; 4.2 Basic approaches in qualitative reasoning; 4.3 Qualitative Model; 4.4 Qualitative Process; 4.5 Qualitative Simulation Reasoning; 4.5.1 Qualitative state transformation; 4.5.2 QSIM algorithm; 4.6 Algebra Approach; 4.7 Spatial Geometric Qualitative Reasoning.…”
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The neural basis of free will : criterial causation by Tse, Peter

Table of Contents: “…Implications of Criterial Causality for Mental Representation -- The Neural Code Is Not Algorithmic -- Criterialism, Descriptivism, and Reference -- Countering Kripke's Attack -- Wittgenstein and Criteria -- Propositions and Vectorial Encodings -- Mental Operations versus Mental Representations -- Beyond Functionalism -- 9. …”
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Dermatologic principles and practice in oncology : conditions of the skin, hair, and nails in cancer patients

Table of Contents: “…Borovicka, Jennifer R.S. Gordon, Ann Cameron Haley, Nicole E. Larson and Dennis P. …”
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Computational ecology : artificial neural networks and their applications by Zhang, Wenjun

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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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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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QoS and Energy Management in Cognitive Radio Network : Case Study Approach. by Mishra, Vishram

Table of Contents: “…; 1.2 Spectrum Regulation; 1.2.1 Licensed Spectrum; 1.2.2 Unlicensed Spectrum; 1.2.3 Open Spectrum; 1.3 Opportunistic Spectrum Usage; 1.4 Software Defined Radio and Cognitive Radio; 1.4.1 IEEE Groups Working on Spectrum Sharing; 1.4.2 Cognition Cycle; 1.4.3 Cognitive Engine and Framework; 1.4.4 Cognitive Radio Network; 1.5 Quality of Service (QoS); 1.5.1 QoS Provisioning for Latency Guarantee; 1.5.2 QoS Provisioning for Throughput Guarantee; 1.6 Channel Selection Techniques in Cognitive Radio Network.…”
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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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Artificial intelligence in society.

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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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Haptic Feedback Teleoperation of Optical Tweezers by Ni, Zhenjiang

Table of Contents: “…Specific designs for haptic interactions; 1.4.1. Temporal sharing; 1.4.2. Spatial sharing; 1.5. Discussion; 1.6. …”
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Machine Learning with Spark - Second Edition. by Dua, Rajdeep

Table of Contents: “…Generating predictions for the Kaggle/StumbleUpon evergreen classification dataset -- Evaluating the performance of classification models -- Accuracy and prediction error -- Precision and recall -- ROC curve and AUC -- Improving model performance and tuning parameters -- Feature standardization -- Additional features -- Using the correct form of data -- Tuning model parameters -- Linear models -- Iterations -- Step size -- Regularization -- Decision trees -- Tuning tree depth and impurity -- The naive Bayes model -- Cross-validation -- Summary -- Chapter 7: Building a Regression Model with Spark -- Types of regression models -- Least squares regression -- Decision trees for regression -- Evaluating the performance of regression models -- Mean Squared Error and Root Mean Squared Error -- Mean Absolute Error -- Root Mean Squared Log Error -- The R-squared coefficient -- Extracting the right features from your data -- Extracting features from the bike sharing dataset -- Training and using regression models -- BikeSharingExecutor -- Training a regression model on the bike sharing dataset -- Linear regression -- Generalized linear regression -- Decision tree regression -- Ensembles of trees -- Random forest regression -- Gradient boosted tree regression -- Improving model performance and tuning parameters -- Transforming the target variable -- Impact of training on log-transformed targets -- Tuning model parameters -- Creating training and testing sets to evaluate parameters -- Splitting data for Decision tree -- The impact of parameter settings for linear models -- Iterations -- Step size -- L2 regularization -- L1 regularization -- Intercept -- The impact of parameter settings for the decision tree -- Tree depth -- Maximum bins -- The impact of parameter settings for the Gradient Boosted Trees -- Iterations -- MaxBins -- Summary.…”
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World's greatest architect : making, meaning, and network culture by Mitchell, William J. (William John), 1944-2010

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Resources utilization and productivity enhancement case studies by Mital, Anil, Pennathur, Arunkumar

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