Machine Learning Optimization Techniques (136 results)

Paperback. Condition: Good. The book has been read but remains in clean condition. All pages are intact and the cover is intact. Some minor wear to the spine.

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hardcover. Condition: Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned.…

Paperback. Condition: Good. Algorithmic Trading Methods: Applications using Advanced Statistics, Optimization, and Machine Learning Techniques, Second Edition, is a sequel to The Science of Algorithmic Trading and Portfolio Management. This edition includes new chapters on algorithmic trading, advanced trading analytics, regression analysis, optimization, and advanced statistical methods. Increasing its focus on trading strategies and models, this edition includes new insights into the ever-changing financial environment, pre-trade and post-trade analysis, liquidation cost & risk analysis, and compliance and regulatory reporting requirements. Highlighting new investment techniques, this book includes material to assist in the best execution process, model validation, quality and assurance testing, limit order modeling, and smart order routing analysis. Includes advanced modeling techniques using machine learning, predictive analytics, and neural networks. The text provides readers with a suite of transaction cost analysis functions packaged as a TCA library. These programming tools are accessible via numerous software applications and programming languages. …

Paperback. Condition: Good. Algorithmic Trading Methods: Applications using Advanced Statistics, Optimization, and Machine Learning Techniques, Second Edition, is a sequel to The Science of Algorithmic Trading and Portfolio Management. This edition includes new chapters on algorithmic trading, advanced trading analytics, regression analysis, optimization, and advanced statistical methods. Increasing its focus on trading strategies and models, this edition includes new insights into the ever-changing financial environment, pre-trade and post-trade analysis, liquidation cost & risk analysis, and compliance and regulatory reporting requirements. Highlighting new investment techniques, this book includes material to assist in the best execution process, model validation, quality and assurance testing, limit order modeling, and smart order routing analysis. Includes advanced modeling techniques using machine learning, predictive analytics, and neural networks. The text provides readers with a suite of transaction cost analysis functions packaged as a TCA library. These programming tools are accessible via numerous software applications and programming languages. …

Paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

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Taschenbuch. Condition: Neu. Neuware.

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Paperback. Condition: Brand New. 109 pages. 11.69x8.27x0.28 inches. In Stock.

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Taschenbuch. Condition: Neu. Neuware - 'Tensor Conversion Techniques with Hummingbird: Efficient Strategies for Machine Learning Model Transformation and Optimization' serves as an essential resource for data scientists, machine learning engineers, and systems architects aiming to master the art of transforming classical ML models into optimized tensor computations. This guide begins by establishing a strong foundation in tensor mathematics, covering critical concepts such as data layouts, storage patterns, numerical precision techniques, and seamless interoperability among leading tensor libraries like NumPy, PyTorch, ONNX, and TVM. Readers gain a nuanced understanding of the opportunities and challenges presented by real-world tensor representations, setting the stage for practical application. Central to the book is an in-depth exploration of the Hummingbird architecture-an innovative open-source library that automates the conversion of traditional ML models from frameworks like scikit-learn, LightGBM, and XGBoost into efficient tensorized forms. Detailed walkthroughs illustrate Hummingbird's step-by-step conversion pipeline, highlighting the role of intermediate representations in enabling robust operator mapping. The text also offers actionable insights into optimizing integration with popular backends such as PyTorch and ONNX, advanced graph-level optimizations, parallelization strategies, and deployment techniques tailored for modern heterogeneous hardware environments, empowering readers to scale tensorized models with confidence. Beyond the technical intricacies of model conversion, this volume emphasizes production-grade best practices, encompassing comprehensive testing and validation workflows-including unit testing, numerical stability checks, and automated regression analysis-alongside security considerations like integrity verification, threat modeling, and compliance auditing. Concluding chapters shine a light on the future of tensor conversion, exploring emerging trends such as automated optimization powered by machine learning, federated and distributed processing pipelines, explainability in tensorized systems, and evolving standards driving model interoperability. Whether modernizing legacy pipelines or architecting next-generation scalable ML infrastructure, this book equips professionals with a holistic, expert approach to efficient model transformation and optimization.…

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Paperback. Condition: Brand New. 232 pages. 9.18x6.12x9.21 inches. In Stock.

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Condition: Wie Neu. Zustandsbeschreibung: leichte Lagerspuren, leicht bestoßen/minor shelfwear, slightly bumped. Edited by Vipin Kumar Kukkala and Sudeep Pasricha. With contributions by Wanli Chang, Nan Chen, Shuai Zhao, Xiaotian Dai et al. XV,789 Seiten, gebunden (Springer-Verlag 2023). Statt EUR 117,69. Gewicht: 137 g - Gebunden/Gebundene Ausgabe.…

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Condition: New. 2024. Paperback. . . . . .