deep learning hilbert spaces di flux jamie (6 risultati)

Condizione: New. In.

Taschenbuch. Condizione: Neu. Neuware.

Paperback. Condizione: new. Paperback. This book delves into the fusion of advanced mathematical concepts and cutting-edge deep learning techniques to transform algorithmic trading. By extending deep learning models into Hilbert spaces-complete infinite-dimensional spaces endowed with inner products-the book presents a novel framework for handling the complex, high-dimensional data inherent in financial markets. This approach opens new avenues for modeling and predicting market behaviors with greater accuracy and computational efficiency. Main Topics: Foundations of Hilbert Spaces in Financial Modeling: This section introduces the core principles of Hilbert spaces and their applicability to finance, explaining how infinite-dimensional spaces can model complex financial phenomena more effectively than traditional finite-dimensional methods.Extending Deep Learning Architectures to Hilbert Spaces: Exploring how standard deep learning models like neural networks can be generalized to operate within Hilbert spaces, enabling the processing of functional data and continuous-time signals crucial for high-frequency trading.Kernel Methods and Reproducing Kernel Hilbert Spaces (RKHS): Discussing the role of RKHS in enhancing machine learning models, particularly in capturing nonlinear relationships in financial data through kernel functions that map inputs into higher-dimensional Hilbert spaces. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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

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

Paperback. Condizione: new. Paperback. This book delves into the fusion of advanced mathematical concepts and cutting-edge deep learning techniques to transform algorithmic trading. By extending deep learning models into Hilbert spaces-complete infinite-dimensional spaces endowed with inner products-the book presents a novel framework for handling the complex, high-dimensional data inherent in financial markets. This approach opens new avenues for modeling and predicting market behaviors with greater accuracy and computational efficiency. Main Topics: Foundations of Hilbert Spaces in Financial Modeling: This section introduces the core principles of Hilbert spaces and their applicability to finance, explaining how infinite-dimensional spaces can model complex financial phenomena more effectively than traditional finite-dimensional methods.Extending Deep Learning Architectures to Hilbert Spaces: Exploring how standard deep learning models like neural networks can be generalized to operate within Hilbert spaces, enabling the processing of functional data and continuous-time signals crucial for high-frequency trading.Kernel Methods and Reproducing Kernel Hilbert Spaces (RKHS): Discussing the role of RKHS in enhancing machine learning models, particularly in capturing nonlinear relationships in financial data through kernel functions that map inputs into higher-dimensional Hilbert spaces. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.