GPU Mastery Series: Unlocking CUDA's Power using pyCUDA (32 résultats)

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Paperback. Etat : new. Paperback. Unlock the true potential of GPU acceleration in image processing and computer vision with this comprehensive guide. Designed for researchers, practitioners, and advanced students, this book delves deep into cutting-edge algorithms optimized using pyCUDA, offering unparalleled performance improvements for real-world applications.Key Features: In-Depth Exploration of Advanced Algorithms: Each chapter provides a meticulous analysis of specific, state-of-the-art algorithms, pushing the boundaries of current knowledge and exploring uncharted territories in the field.Optimization with pyCUDA: Learn how to harness the massive parallelism of CUDA-enabled GPUs using pyCUDA, transforming computational workflows for real-time processing.Innovative Methodologies: Discover original theoretical frameworks, novel methodologies, and interdisciplinary perspectives that challenge the status quo and inspire new horizons.Practical Implementation Details: Gain insights into optimizing memory management, thread synchronization, and kernel configurations to maximize computational efficiency.Sample Topics Covered: Optimized Convolutional Filtering Techniques: Implement convolutional filters like Gaussian and Laplacian kernels using pyCUDA, achieving real-time performance even on high-resolution images through optimized memory access and data transfer strategies.Adaptive Edge Detection with Dynamic Thresholding: Explore novel adaptive edge detection algorithms employing dynamic thresholding mechanisms that adjust in real-time based on local image statistics, enhancing accuracy in varying illumination and noise conditions.Advanced Image Segmentation with Graph-Based Methods: Model images as weighted graphs and implement parallel algorithms for graph construction and label propagation, utilizing spectral clustering and community detection techniques optimized for GPU architectures.Accelerated Histogram Equalization and Contrast Enhancement: Learn to compute histograms and cumulative distribution functions in parallel, implementing adaptive methods like Contrast Limited Adaptive Histogram Equalization (CLAHE) for efficient image enhancement.Feature Detection and Description with SURF and SIFT Algorithms: Master the implementation of Speeded-Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT) on GPUs, optimizing integral image computations and descriptor matching for real-time applications.Advanced Optical Flow Estimation: Dive into optical flow computation using Lucas-Kanade and Horn-Schunck methods, optimized for GPUs to handle large displacements and occlusions with real-time performance.Stereo Vision and Depth Map Estimation: Implement depth estimation techniques using block matching and semi-global matching methods, optimizing cost aggregation and handling of occlusions for high-resolution stereo images.Wavelet Transformations for Multi-Resolution Processing: Utilize discrete wavelet transforms for tasks like denoising and compression, implementing both 1D and 2D transformations efficiently on GPUs.Real-Time Object Recognition with HOG Features: Accelerate object recognition using Histogram of Oriented Gradients (HOG) descriptors, optimizing gradient histograms and detection strategies for applications like pedestrian and vehicle recognition.Image Registration Techniques Using Mutual Information: Apply multi-modal image registration using mutual information metrics, optimizing joint histogram estimation and transformation handling for applications in medical ima Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Etat : new. Paperback. Unlock the future of computational plasma physics with this unparalleled exploration of advanced simulation algorithms for nuclear fusion modeling. Highlights Include: Adaptive Mesh Refinement Particle-in-Cell Algorithms: Discover sophisticated techniques for plasma turbulence simulation that dynamically refine computational meshes in regions of high activity, optimizing resources and capturing fine-scale phenomena with unprecedented accuracy. Implicit Hybrid Kinetic-Fluid Methods: Explore innovative algorithms that couple kinetic and fluid models through implicit time integration schemes, enabling efficient multiscale plasma dynamics simulation. Delve into methods that dynamically exchange information between kinetic and fluid domains, ensuring stability and accuracy in long-term plasma behavior modeling. Spectral Element Methods for Gyrokinetic Equations: Learn about high-order spectral element algorithms designed to solve gyrokinetic equations with exceptional precision. Understand how these methods handle complex geometries and capture essential microturbulence effects in magnetically confined plasmas. Entropy-Based Closure Models: Examine advanced algorithms employing entropy maximization to derive closure relations in kinetic simulations. Gain insights into how these models preserve thermodynamic properties and accurately represent non-equilibrium plasma behaviors, crucial for collisionless process modeling. Artificial Neural Network Accelerated Simulations: Delve into algorithms that integrate artificial neural networks to accelerate plasma simulations by learning and predicting computationally intensive components. Understand how surrogate models embedded within simulation frameworks can significantly reduce computational time without sacrificing accuracy. Symplectic Integrators for Long-Term Dynamics: Study numerical algorithms utilizing symplectic integrators to preserve the Hamiltonian structure of plasma equations over extended periods. Discover methods that conserve invariants of motion, minimizing numerical dissipation and ensuring accurate energy conservation critical for stability studies. Multilevel Preconditioners and Matrix-Free Solvers: Explore advanced computational techniques that enhance scalability and efficiency in large-scale plasma simulations. Learn about multilevel preconditioners that accelerate convergence and matrix-free solvers that reduce memory usage on supercomputing architectures. Anisotropic Heat Flux Limiters and Non-Fourier Heat Conduction Models: Investigate algorithms that incorporate anisotropic heat flux limiters to model thermal transport accurately and those that simulate non-Fourier heat conduction in ultra-fast processes, capturing finite speeds of thermal propagation essential for transient heating events. Uncertainty Quantification and Data-Driven Modeling: Understand how sparse grid collocation methods and polynomial chaos expansions are employed for uncertainty quantification in plasma simulations. Examine data-driven algorithms that build reduced-order models from high-fidelity data to accelerate simulations while maintaining predictive capability. Multi-Physics Coupling and Integrated Simulations: Gain insights into algorithms that seamlessly couple multiple physical models-plasma dynamics, electromagnetics, material responses-into integrated simulations, offering comprehensive perspectives on fusion systems under realistic conditions. Embark on a journey through the forefront of plasma simulation technology. This work is not just a collection of algorithms; it's an invitation to pionee Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Etat : new. Paperback. Unlock the future of computational plasma physics with this unparalleled exploration of advanced simulation algorithms for nuclear fusion modeling. Highlights Include: Adaptive Mesh Refinement Particle-in-Cell Algorithms: Discover sophisticated techniques for plasma turbulence simulation that dynamically refine computational meshes in regions of high activity, optimizing resources and capturing fine-scale phenomena with unprecedented accuracy. Implicit Hybrid Kinetic-Fluid Methods: Explore innovative algorithms that couple kinetic and fluid models through implicit time integration schemes, enabling efficient multiscale plasma dynamics simulation. Delve into methods that dynamically exchange information between kinetic and fluid domains, ensuring stability and accuracy in long-term plasma behavior modeling. Spectral Element Methods for Gyrokinetic Equations: Learn about high-order spectral element algorithms designed to solve gyrokinetic equations with exceptional precision. Understand how these methods handle complex geometries and capture essential microturbulence effects in magnetically confined plasmas. Entropy-Based Closure Models: Examine advanced algorithms employing entropy maximization to derive closure relations in kinetic simulations. Gain insights into how these models preserve thermodynamic properties and accurately represent non-equilibrium plasma behaviors, crucial for collisionless process modeling. Artificial Neural Network Accelerated Simulations: Delve into algorithms that integrate artificial neural networks to accelerate plasma simulations by learning and predicting computationally intensive components. Understand how surrogate models embedded within simulation frameworks can significantly reduce computational time without sacrificing accuracy. Symplectic Integrators for Long-Term Dynamics: Study numerical algorithms utilizing symplectic integrators to preserve the Hamiltonian structure of plasma equations over extended periods. Discover methods that conserve invariants of motion, minimizing numerical dissipation and ensuring accurate energy conservation critical for stability studies. Multilevel Preconditioners and Matrix-Free Solvers: Explore advanced computational techniques that enhance scalability and efficiency in large-scale plasma simulations. Learn about multilevel preconditioners that accelerate convergence and matrix-free solvers that reduce memory usage on supercomputing architectures. Anisotropic Heat Flux Limiters and Non-Fourier Heat Conduction Models: Investigate algorithms that incorporate anisotropic heat flux limiters to model thermal transport accurately and those that simulate non-Fourier heat conduction in ultra-fast processes, capturing finite speeds of thermal propagation essential for transient heating events. Uncertainty Quantification and Data-Driven Modeling: Understand how sparse grid collocation methods and polynomial chaos expansions are employed for uncertainty quantification in plasma simulations. Examine data-driven algorithms that build reduced-order models from high-fidelity data to accelerate simulations while maintaining predictive capability. Multi-Physics Coupling and Integrated Simulations: Gain insights into algorithms that seamlessly couple multiple physical models-plasma dynamics, electromagnetics, material responses-into integrated simulations, offering comprehensive perspectives on fusion systems under realistic conditions. Embark on a journey through the forefront of plasma simulation technology. This work is not just a collection of algorithms; it's an invitat Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Etat : new. Paperback. Unlock the true potential of GPU acceleration in image processing and computer vision with this comprehensive guide. Designed for researchers, practitioners, and advanced students, this book delves deep into cutting-edge algorithms optimized using pyCUDA, offering unparalleled performance improvements for real-world applications.Key Features: In-Depth Exploration of Advanced Algorithms: Each chapter provides a meticulous analysis of specific, state-of-the-art algorithms, pushing the boundaries of current knowledge and exploring uncharted territories in the field.Optimization with pyCUDA: Learn how to harness the massive parallelism of CUDA-enabled GPUs using pyCUDA, transforming computational workflows for real-time processing.Innovative Methodologies: Discover original theoretical frameworks, novel methodologies, and interdisciplinary perspectives that challenge the status quo and inspire new horizons.Practical Implementation Details: Gain insights into optimizing memory management, thread synchronization, and kernel configurations to maximize computational efficiency.Sample Topics Covered: Optimized Convolutional Filtering Techniques: Implement convolutional filters like Gaussian and Laplacian kernels using pyCUDA, achieving real-time performance even on high-resolution images through optimized memory access and data transfer strategies.Adaptive Edge Detection with Dynamic Thresholding: Explore novel adaptive edge detection algorithms employing dynamic thresholding mechanisms that adjust in real-time based on local image statistics, enhancing accuracy in varying illumination and noise conditions.Advanced Image Segmentation with Graph-Based Methods: Model images as weighted graphs and implement parallel algorithms for graph construction and label propagation, utilizing spectral clustering and community detection techniques optimized for GPU architectures.Accelerated Histogram Equalization and Contrast Enhancement: Learn to compute histograms and cumulative distribution functions in parallel, implementing adaptive methods like Contrast Limited Adaptive Histogram Equalization (CLAHE) for efficient image enhancement.Feature Detection and Description with SURF and SIFT Algorithms: Master the implementation of Speeded-Up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT) on GPUs, optimizing integral image computations and descriptor matching for real-time applications.Advanced Optical Flow Estimation: Dive into optical flow computation using Lucas-Kanade and Horn-Schunck methods, optimized for GPUs to handle large displacements and occlusions with real-time performance.Stereo Vision and Depth Map Estimation: Implement depth estimation techniques using block matching and semi-global matching methods, optimizing cost aggregation and handling of occlusions for high-resolution stereo images.Wavelet Transformations for Multi-Resolution Processing: Utilize discrete wavelet transforms for tasks like denoising and compression, implementing both 1D and 2D transformations efficiently on GPUs.Real-Time Object Recognition with HOG Features: Accelerate object recognition using Histogram of Oriented Gradients (HOG) descriptors, optimizing gradient histograms and detection strategies for applications like pedestrian and vehicle recognition.Image Registration Techniques Using Mutual Information: Apply multi-modal image registration using mutual information metrics, optimizing joint histogram estimation and transformation handling for applications i Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Paperback. Etat : new. Paperback. Unlock the future of signal processing with this comprehensive volume that delves deep into advanced algorithms for multi-dimensional data analysis. This book serves as an indispensable resource for researchers, engineers, and advanced practitioners seeking to harness cutting-edge computational techniques in signal processing. Spanning 99 meticulously crafted chapters, it explores an extensive array of sophisticated algorithms and theoretical frameworks that push the boundaries of current knowledge. Each chapter presents a deep dive into a specific algorithm, offering innovative ideas, original theories, and insights into the latest research breakthroughs. Highlights Include: Optimized Multi-dimensional Discrete Fourier Transform: Discover advanced optimization techniques for implementing the multi-dimensional DFT on large-scale datasets. Learn about novel approaches that reduce computational complexity by exploiting symmetries and utilizing sparse representations. Accelerated Multi-dimensional Fast Fourier Transform Algorithms: Explore cutting-edge methods that build upon the foundational Cooley-Tukey algorithm. Understand how to partition data domains into optimized sub-regions, enabling parallel processing without sacrificing accuracy. Sparse Representation and Compressed Sensing in Multi-dimensional Signals: Delve into groundbreaking algorithms that leverage tensor decomposition methods for ultra-sparse representations. Applications discussed include medical imaging reconstruction and remote sensing data analysis. Graph Signal Processing on Multi-dimensional Data Structures: Examine novel algorithms at the intersection of graph theory and signal processing. Learn how to generalize traditional concepts to graph domains, facilitating analysis of complex structures like social networks and biological systems. Deep Learning Architectures for Multi-dimensional Signal Processing: Gain insights into the latest deep learning models, such as multi-dimensional convolutional neural networks and recurrent neural networks adapted for spatial-temporal data. Topological Data Analysis in Multi-dimensional Signal Processing: Explore the application of algebraic topology concepts, like persistent homology, to extract shape-based features from complex data sets, enhancing capabilities in data mining and material science. Advanced Kalman Filter Techniques in Multi-dimensional Spaces: Learn about sophisticated algorithms extending the standard Kalman filter to high-dimensional state spaces, with innovations in adaptive covariance estimation and nonlinear state models. Multi-dimensional Empirical Mode Decomposition Algorithms: Understand how to generalize EMD to higher dimensions, enabling decomposition of complex signals into intrinsic mode functions for applications in image processing and environmental monitoring. Hybrid Time-Frequency Analysis in Multi-dimensional Signal Processing: Discover how to combine the strengths of various transforms within a unified framework, allowing simultaneous analysis of localized and global features in complex applications like seismic data interpretation. Nonlinear Multi-dimensional Signal Processing with Kernel Methods: Investigate algorithms that map high-dimensional signals into reproducing kernel Hilbert spaces, enabling nonlinear operations with linear complexity. Applications in pattern recognition and data compression are discussed. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Etat : new. Paperback. Unlock the forefront of algorithmic day trading with this comprehensive exploration into advanced deep learning techniques. This authoritative volume presents cutting-edge algorithms and innovative methodologies that fuse the complexities of financial markets with the rigor of deep learning architectures. Designed for professional quantitative analysts, algorithmic traders, and advanced researchers, this work delves into sophisticated topics such as: Transformer-Based Multivariate Time Series Forecasting: Harness the power of self-attention mechanisms to capture complex temporal dependencies across multiple financial indicators, enhancing predictive capabilities in volatile markets. Graph Neural Networks for Modeling Inter-stock Relationships: Discover how to represent stocks as nodes within a graph structure, employing spectral graph convolutions and attention mechanisms to model intricate market dynamics and optimize portfolio strategies. Deep Reinforcement Learning with Adversarial Training: Explore algorithms that enhance trading agents' robustness by simulating market manipulations, utilizing minimax formulations and robust optimization techniques to improve decision-making under adverse conditions. Variational Autoencoders for Anomaly Detection: Learn to detect anomalies in stock price movements by modeling uncertainty with probabilistic latent representations, employing hierarchical latent variables and optimizing evidence lower bound (ELBO) metrics. Neural Ordinary Differential Equations for Continuous-Time Financial Modeling: Integrate continuous-time dynamics into neural network architectures to model the fluid nature of financial systems, leveraging advanced mathematical concepts like adjoint sensitivity methods for efficient backpropagation. Meta-Learning for Adaptive Trading Strategies: Implement model-agnostic meta-learning algorithms that enable rapid adaptation to changing market conditions, with detailed discussions on meta-gradient computations and regularization techniques to prevent overfitting. Energy-Based Models for Arbitrage Opportunity Detection: Apply energy-based modeling to identify arbitrage opportunities by assigning energy scores to market states, utilizing contrastive divergence training and gradient computations of energy functions. Each chapter presents thorough mathematical formulations, detailed algorithmic implementations, and practical insights, pushing the boundaries of current knowledge. The text integrates interdisciplinary perspectives, from stochastic differential equations and Bayesian inference to manifold regularization and probabilistic programming. Readers will benefit from: In-depth Theoretical Explanations: Comprehensive coverage of advanced mathematical concepts that underpin modern deep learning algorithms in the context of financial markets. Innovative Algorithmic Strategies: Original approaches and novel methodologies for solving complex problems in algorithmic trading, with practical examples and code implementations. Cutting-Edge Research Integration: Incorporation of the latest research breakthroughs, offering insights into the future of deep learning applications in finance. Elevate your understanding of algorithmic trading and position yourself at the vanguard of financial technology innovation with this essential resource. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.