Automated design machine learning (11 Ergebnisse)

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Taschenbuch. Zustand: Neu. Neuware - Reactive PublishingAlgorithmic trading has entered a new era where machine learning models can analyze vast streams of market data, detect subtle patterns, and adapt strategies faster than traditional rule-based systems. The challenge is not simply building models, it is integrating them into trading frameworks that operate reliably in real markets.Machine Learning for Algorithmic Trading with Python provides a structured guide to designing and implementing data-driven trading systems using modern machine learning techniques and Python-based tools.The book moves beyond theory and focuses on practical architecture: how predictive models translate into signals, how strategies are designed around those signals, and how automated systems execute them within real trading environments.Readers will explore how machine learning interacts with market structure, risk management, and portfolio construction while learning how to implement reproducible research pipelines and deploy algorithmic strategies.Inside the book you will learn how to: - Build predictive market models using supervised and unsupervised learning- Prepare financial datasets for machine learning workflows- Design signal pipelines and strategy logic- Integrate machine learning outputs into algorithmic trading frameworks- Evaluate models using walk-forward testing and robust validation techniques- Construct automated trading systems using Python- Manage execution, risk, and portfolio constraints within systematic strategiesThe book uses practical Python examples and focuses on real implementation challenges faced by quantitative traders and developers.It is designed for: - Quantitative traders and systematic investors- Developers building trading algorithms- Data scientists working with financial time series- Finance professionals interested in machine learning applicationsRather than presenting isolated models, the book emphasizes the complete lifecycle of algorithmic trading systems, from data preparation and model development to strategy deployment and automation.If you want to understand how machine learning integrates with modern algorithmic trading infrastructure, this guide provides a clear and practical roadmap.…

Zustand: Used.

Zustand: Used.

Zustand: New. In English.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Automated Design of Machine Learning and Search Algorithms | Nelishia Pillay (u. a.) | Taschenbuch | Natural Computing Series | xviii | Englisch | 2022 | Springer | EAN 9783030720711 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection. The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field. The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.…

Hardcover. Zustand: Brand New. 205 pages. 9.25x6.10x0.63 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection. The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field.The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection. The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field.The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.…