Machine learning optimization techniques (62 Ergebnisse)

Paperback. Zustand: 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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Paperback. Zustand: 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. …

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paperback. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Taschenbuch. Zustand: Neu. Neuware.

Hardcover. Zustand: Good. 1st Edition.

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paperback. Zustand: New. 1st.

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

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Taschenbuch. Zustand: 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.…

hardcover. Zustand: Very Good.

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

Zustand: 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.…

Hardcover. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Zustand: New. 2024. Paperback. . . . . .

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hardcover. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Zustand: new.

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Zustand: As New. Unread book in perfect condition.

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Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides comprehensive coverage of various solutions that address issues related to real-time performance, security, and robustness in emerging automotive platforms. The authors discuss recent advances towards the goal of enabling reliable, secure, and robust, time-critical automotive cyber-physical systems, using advanced optimization and machine learning techniques. The focus is on presenting state-of-the-art solutions to various challenges including real-time data scheduling, secure communication within and outside the vehicle, tolerance to faults, optimizing the use of resource-constrained automotive ECUs, intrusion detection, and developing robust perception and control techniques for increasingly autonomous vehicles.…

Zustand: New. In English.