Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/903
Title: Deep Learning with Python
Other Titles: A Hands-on Introduction /
Authors: Ketkar, Nikhil. ;
subject: Computer Science;Computer Programming;Programming Languages and Electronic Computers;Mathematical logic. ;;Computers;Computer Science;Computing Methodologies. ;;Programming Techniques;Programming Languages and Compilers and Interpreters
Year: 2017
place: Berkeley, CA :
Publisher: Apress :
Imprint: Apress,
Abstract: Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production. ;
Description: QA75.5-76.95 ;

Printed edition: ; 9781484227657. ;

SpringerLink (Online service) ;


URI: http://localhost/handle/Hannan/903
ISBN: 9781484227664 ;
9781484227657 (print) ;
More Information: XV, 160 p. 93 illus., 65 illus. in color. ; online resource. ;
Appears in Collections:مهندسی فناوری اطلاعات

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Title: Deep Learning with Python
Other Titles: A Hands-on Introduction /
Authors: Ketkar, Nikhil. ;
subject: Computer Science;Computer Programming;Programming Languages and Electronic Computers;Mathematical logic. ;;Computers;Computer Science;Computing Methodologies. ;;Programming Techniques;Programming Languages and Compilers and Interpreters
Year: 2017
place: Berkeley, CA :
Publisher: Apress :
Imprint: Apress,
Abstract: Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production. ;
Description: QA75.5-76.95 ;

Printed edition: ; 9781484227657. ;

SpringerLink (Online service) ;


URI: http://localhost/handle/Hannan/903
ISBN: 9781484227664 ;
9781484227657 (print) ;
More Information: XV, 160 p. 93 illus., 65 illus. in color. ; online resource. ;
Appears in Collections:مهندسی فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9781484227664.pdf8.62 MBAdobe PDFThumbnail
Preview File
Title: Deep Learning with Python
Other Titles: A Hands-on Introduction /
Authors: Ketkar, Nikhil. ;
subject: Computer Science;Computer Programming;Programming Languages and Electronic Computers;Mathematical logic. ;;Computers;Computer Science;Computing Methodologies. ;;Programming Techniques;Programming Languages and Compilers and Interpreters
Year: 2017
place: Berkeley, CA :
Publisher: Apress :
Imprint: Apress,
Abstract: Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production. ;
Description: QA75.5-76.95 ;

Printed edition: ; 9781484227657. ;

SpringerLink (Online service) ;


URI: http://localhost/handle/Hannan/903
ISBN: 9781484227664 ;
9781484227657 (print) ;
More Information: XV, 160 p. 93 illus., 65 illus. in color. ; online resource. ;
Appears in Collections:مهندسی فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9781484227664.pdf8.62 MBAdobe PDFThumbnail
Preview File