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Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs
Master the various programming algorithms required
Carry out multi-threaded gradient computations and memory allocations for this threading
Work with CUDA code implementations of all core computations, including layer activations and gradient calculations
Make use of the CONVNET program and manual to explore convolutional nets and case studies
Build deep feedforward nets using locally connected layers, pooling layers, and softmax outputs
Master the various programming algorithms required
Carry out multi-threaded gradient computations and memory allocations for this threading
Work with CUDA code implementations of all core computations, including layer activations and gradient calculations
Make use of the CONVNET program and manual to explore convolutional nets and case studies
Timothy Masters received a PhD in mathematical statistics with a specialization in numerical computing. Since then he has continuously worked as an independent consultant for government and industry. His early research involved automated feature detection in high-altitude photographs while he developed applications for flood and drought prediction, detection of hidden missile silos, and identification of threatening military vehicles. Later he worked with medical researchers in the development of computer algorithms for distinguishing between benign and malignant cells in needle biopsies. For the last twenty years he has focused primarily on methods for evaluating automated financial market trading systems. He has authored five books on practical applications of predictive modeling: Practical Neural Network Recipes in C++ (Academic Press, 1993); Signal and Image Processing with Neural Networks (Wiley, 1994); Advanced Algorithms for Neural Networks (Wiley,1995); Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995); Data Mining Algorithms in C++ (Apress, 2018); Assessing and Improving Prediction and Classification (Apress, 2018); Deep Belief Nets in C++ and CUDA C: Volume 1 (Apress, 2018); and Deep Belief Nets in C++ and CUDA C: Volume 2 (Apress, 2018).
Author is an authority on numerical C++ and algorithms in practice
A practical book with source code and algorithms on deep learning with C++ and CUDA C
Final third of three books in a series on C++ and CUDA C deep learning and belief nets
| Erscheinungsjahr: | 2018 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xii
176 S. 13 s/w Illustr. 176 p. 13 illus. |
| ISBN-13: | 9781484237205 |
| ISBN-10: | 148423720X |
| Sprache: | Englisch |
| Herstellernummer: | 978-1-4842-3720-5 |
| Einband: | Kartoniert / Broschiert |
| Autor: | Masters, Timothy |
| Auflage: | First Edition |
| Hersteller: | APRESS |
| Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
| Maße: | 254 x 178 x 11 mm |
| Von/Mit: | Timothy Masters |
| Erscheinungsdatum: | 05.07.2018 |
| Gewicht: | 0,366 kg |
Timothy Masters received a PhD in mathematical statistics with a specialization in numerical computing. Since then he has continuously worked as an independent consultant for government and industry. His early research involved automated feature detection in high-altitude photographs while he developed applications for flood and drought prediction, detection of hidden missile silos, and identification of threatening military vehicles. Later he worked with medical researchers in the development of computer algorithms for distinguishing between benign and malignant cells in needle biopsies. For the last twenty years he has focused primarily on methods for evaluating automated financial market trading systems. He has authored five books on practical applications of predictive modeling: Practical Neural Network Recipes in C++ (Academic Press, 1993); Signal and Image Processing with Neural Networks (Wiley, 1994); Advanced Algorithms for Neural Networks (Wiley,1995); Neural, Novel, and Hybrid Algorithms for Time Series Prediction (Wiley, 1995); Data Mining Algorithms in C++ (Apress, 2018); Assessing and Improving Prediction and Classification (Apress, 2018); Deep Belief Nets in C++ and CUDA C: Volume 1 (Apress, 2018); and Deep Belief Nets in C++ and CUDA C: Volume 2 (Apress, 2018).
Author is an authority on numerical C++ and algorithms in practice
A practical book with source code and algorithms on deep learning with C++ and CUDA C
Final third of three books in a series on C++ and CUDA C deep learning and belief nets
| Erscheinungsjahr: | 2018 |
|---|---|
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xii
176 S. 13 s/w Illustr. 176 p. 13 illus. |
| ISBN-13: | 9781484237205 |
| ISBN-10: | 148423720X |
| Sprache: | Englisch |
| Herstellernummer: | 978-1-4842-3720-5 |
| Einband: | Kartoniert / Broschiert |
| Autor: | Masters, Timothy |
| Auflage: | First Edition |
| Hersteller: | APRESS |
| Verantwortliche Person für die EU: | APress in Springer Science + Business Media, Heidelberger Platz 3, D-14197 Berlin, juergen.hartmann@springer.com |
| Maße: | 254 x 178 x 11 mm |
| Von/Mit: | Timothy Masters |
| Erscheinungsdatum: | 05.07.2018 |
| Gewicht: | 0,366 kg |
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